Systems and methods for behavioral monitoring and calibration

ABSTRACT

Systems and methods for the analysis of the diverse behaviors of animal subjects in defined areas are provided, including tools for filtering and analysis of high-resolution behavioral data. These systems and methods provide an opportunity to examine behavioral patterns with levels of precision and quantization that have not been previously achieved. Methods and systems for managing and analyzing the very large and unique datasets produced by behavioral monitoring systems, including quality assessment and control, archiving, data query, data reduction, analytical procedures and visualization techniques are provided. Such detailed analyses of spontaneous behavior provide fundamental insights into the neural organization of behavior and enable detection of genetic, pharmacological and environmental influences on brain function with high sensitivity.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit of and priority to U.S. Ser. No.61/062,173, filed on Jan. 23, 2008, which is incorporated herein byreference in its entirety for all purposes.

STATEMENT OF GOVERNMENT SUPPORT

This invention was made with government support under grant no. K08MH071671, awarded by the National Institutes of Health. The governmenthas certain rights in the invention.

BACKGROUND OF THE INVENTION

To survive and reproduce, animals acting in their natural environmentsmust engage in a variety of behaviors such as procuring food, escapingpredators, and seeking shelter or sexual partners. Because environmentalconstraints determine the most suitable times and places to performspecific behaviors and because many behaviors cannot be performedsimultaneously, it is essential for animals to appropriately prioritizeand organize when and where to engage in a particular behavior. As aresult, the organization of behavior in a freely acting animalrepresents an adaptation to the environment This organization depends onthe integrative activities of the central nervous system and reflectsthe functions and interactions of a diverse array of physiological andbehavioral systems such as those regulating energy balance, thermalstatus, osmotic/volume status, sleep, reproduction, defense, andenvironmental entrainment. The ability to monitor and characterize theorganization of behavior in a freely acting animal thus has thepotential to provide a sensitive assay for examining the functions andinteractions of numerous physiological and behavioral systems.

Substantial limitations currently exist in our ability to apply recentbiotechnological advances to analyze neural substrates of complexmammalian behavior. In contrast to the rapid pace of innovation seen inthe fields of mammalian genomics, medicinal chemistry and informationtechnology, less progress has been made in the development of behavioralassessment techniques for mice or other mammals. Such procedures arevital for exploring the impact of genes, drugs and environment on brainfunctions relevant to common neuropsychiatric conditions such asschizophrenia, depression, and anxiety. Standard approaches, for exampleinvolving repeated removal of mice from their home cages for a batteryof behavioral tests, are problematic because: 1) they are time-consumingand labor-intensive, 2) the order of test administration can skew theresulting data, 3) removal of mice from the home cage produces stressthat confounds interpretation of behavioral data, and 4) data arefrequently misinterpreted due to a failure to consider behavioraldomains that are not the main focus of study (e.g.: impact of anxiety ontests of learning).

SUMMARY OF THE INVENTION

Systems and methods for the continuous monitoring of the diversebehaviors of animal subjects in defined areas are provided, includingtools for filtering and analysis of high-resolution behavioral data.These systems and methods provide an opportunity to examine behavioralpatterns with levels of precision and quantization that have not beenpreviously achieved. Methods and systems for managing and analyzing thevery large and unique datasets produced by behavioral monitoringsystems, including quality assessment and control, archiving, dataquery, data reduction, analytical procedures and visualizationtechniques are provided. Such detailed analyses of spontaneous behaviorprovide fundamental insights into the neural organization of behaviorand enable detection of genetic, pharmacological and environmentalinfluences on brain function with high sensitivity.

One aspect of the invention relates to methods of quality assessment andfiltering of behavioral data. In certain embodiments, the methodsinvolve detecting inconsistencies between position tracking informationand information about interaction with one or more devices and/ordetecting inconsistencies in information about interaction with multipledevices. For example, in certain embodiments, a behavioral datasetincluding animal behavioral data collected over a measurement periodusing a measurement system, including event information regardingspatial position of an animal subject in a defined measurement area,device event information regarding behavior of the animal subject at orwith a plurality of devices at known locations in the defined area, andtemporal information associated with the position and device eventinformation is received. The methods involve analyzing the behavioraldata to detect 1) position information inconsistent with device eventinformation, with said detection is based on the known location of thedevices, and/or 2) device event information for one or more devicesinconsistent with device event information for any other device, withsaid detection is based on temporal information associated with thedevice events; and updating the data based on at least some of thedetected inconsistencies.

In particular embodiments, filtering the behavioral data set may involvereceiving the collected behavioral data; identifying false device eventonsets and removing associated device event information; calculatingcorrections to the position information by comparing the positioninformation during at least some device events with the expectedposition of the animal based on the known location of the device;updating position information based on the calculated corrections; andidentifying and removing data resulting from failure of the measurementsystem to detect termination of a device event.

Another aspect of the invention relates to organizing or classifyinganimal behavior into states, e.g., active and inactive states. Accordingto certain embodiments, automated methods are provided that involveidentifying transitions between active states and inactive states of theanimal subject using spatial (e.g., position tracking) and temporalinformation received from a behavioral monitoring system. Also providedare methods of analyzing animal behavioral data collected using ameasurement system, said behavioral data comprising spatial and temporalinformation regarding the position of the animal in a definedmeasurement area, the methods involving using the spatial information toidentify transitions between active and inactive states by determiningthe location of the longest duration between animal subject movementsduring a time period.

Another aspect of the invention relates to behavioral boutclassification. In certain embodiments, automated methods of analyzing aset of animal subject behavioral data collected over a measurementperiod using a measurement system are provided. The automated methodsinvolve receiving position tracking information for the animal subjectin a defined area during the measurement period and information abouttemporal patterns of one or more behaviors during the measurementperiod; and using the position tracking information and the temporalinformation to identify bouts of the one or more behaviors.

In certain embodiments, method of analyzing a set of animal subjectbehavioral data collected over a measurement period using a measurementsystem that involve receiving spatial information regarding the spatialposition of the animal subject during the measurement period andinformation about temporal patterns of one or more behaviors during themeasurement period; using the spatial information and the temporalinformation to identify bouts of the one or more behaviors, wherein thespatial information comprises information about the spatial position ofthe animal subject during events and inter-event intervals, wherein aninter-event interval is the interval between consecutive device eventsat a device, are provided.

Another aspect of the invention relates to comparing two groups ofanimal behavioral data (e.g., a control and a test group). In certainembodiments, the methods involve clustering the combined data for twogroups and determining the cluster that contributes most to thedifference to the two groups. In particular embodiments, the methodsinvolve receiving a test dataset having behavioral data associated witha group of test animal subjects; receiving a control dataset havingbehavioral data associated with a group of control animal subjects;combining the behavioral data from the test and control datasets;clustering the combined dataset into a selected number of clusters;calculating a chi-square statistic for each cluster based on thehypothesis that the behavioral data in the control and test data sets isthe same; summing the chi-square statistic for all clusters to obtain ameasure of the difference between the test group data and the controlgroup data; obtaining a measure of the significant of the difference bypermuting data for the animal subjects between the test and controlgroups; and if the difference is statistically significant, determiningthe clusters that contribute most to the difference.

According to various embodiments, patterns of behavior that may becompared include patterns of movement, patterns of feeding, patterns ofdrinking, patterns of drug ingestion, patterns of other ingestivebehaviors, patterns of sleeping, patterns of contact with a test object,and patterns of response to another animal or other sensory stimuli.Physiological measurements, e.g., indicating behavioral measurements orresponses, may also be compared, including, heart rate, metabolic rate,blood pressure and body temperature.

Also provided are computer program products including machine-readablemedia on which are stored program instructions for implementing at leastsome portion of the methods described above. Any of the methodsdescribed herein may be represented, in whole or in part, as programinstructions that can be provided on such computer readable media. Alsoprovided are various combinations of data and data structures generatedand/or used as described herein.

These and other features and advantages will be described in more detailbelow with reference to the associated figures

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram presenting certain operations employed in amethod of filtering movement and device event data collected from abehavioral monitoring system in accordance with various embodiments ofthe present invention.

FIG. 2 is a screen shot depicting a 24-hr mouse behavioral record inwhich positions are indicated in green, feeding event locations inorange, and drinking event locations in blue.

FIG. 3 is a flow diagram presenting certain operations employed in amethod of detecting false device events (event onset errors) inaccordance with various embodiments of the present invention.

FIG. 4 is a flow diagram presenting certain operations in a method ofdetermining overall drift in position measurements. In certainembodiments, overall position drift is used to detect false deviceevents.

FIG. 5A is a screen shot depicting graphs on which the differencesbetween the overall position drift and lickometer event drifts (blue)and photobeam event drifts (red) over a 24-hr monitoring period areplotted. X and Y axis drifts are plotted separately.

FIG. 5B is a screen shot depicting a mouse behavioral record in whichlocomotor path is indicated in green, feeding event locations in orange,and drinking event locations in blue. Potential false drinking eventsare flagged for user review.

FIG. 6 is a flow diagram presenting certain operations employed in amethod of correcting movement/position data using the known locations ofdevices in accordance with various embodiments of the present invention.

FIG. 7 is a flow diagram presenting certain operations employed in amethod of detecting the failure of a device event to terminate inaccordance with various embodiments of the present invention.

FIG. 8 is a screen shot depicting a mouse behavioral record showing acluster of feeding events near the feeder (green squares) and a clusterof drinking events near the lickometer (blue circles). The two squaresin the opposite corner represent the maximum distances of the animalfrom the feeder determined during two feeding events. The red coloringindicates that they fall outside the criteria for valid feeding events.

FIG. 9 is a screen shot depicting a mouse behavioral record in whichpositions are indicated in green, feeding event locations in orange, anddrinking event locations in blue. The positions are heavily skewedtoward one end of measurement area indicating position detector (loadbeam) malfunction.

FIG. 10 is a flow diagram presenting certain operations employed in amethod of detecting position detector malfunction in accordance withvarious embodiments of the present invention.

FIGS. 11 and 12 are flow diagrams presenting certain operation employedin a method of classifying active and inactive states of animalsubject(s) from movement and device event data collected from abehavioral monitoring system in accordance with various embodiments ofthe present invention.

FIG. 13 shows examples of 1) a plot 1301 of distance from the longestpause vs. pause duration at a device, 2) a 3-line curve fit (1303) todetermine the inactive state pause threshold, and 3) a graphicaldepiction (1305) of the location of inactive state positions asdetermined using the inactive state pause threshold.

FIG. 14 is a flow diagram presenting certain operations employed in amethod of calculating state classification error to optimize the timewindow/movement threshold in accordance with various embodiments of thepresent invention.

FIG. 15 is a flow diagram presenting certain operations employed in amethod of classifying bouts of animal behavior from movement and deviceevent data collected from a behavioral monitoring system in accordancewith various embodiments of the present invention.

FIGS. 16A-16D shows screen shots depicting a user interface for dataquality control. Panel A shows a screen with experiment round, mouse anddata selection boxes. For this particular mouse-day selected, twoflagged error events are listed. Panel B shows the Supplemental PlotChooser on left, which enables viewing of multiple features of the data.The Drift Difference option is selected and corresponding plot shown onright. Panels C and D show two examples of the Main Screen eachcontaining an event plot (bottom), a quiver plot showing animalpositions (upper right), and the layout of quality control buttons,navigation buttons and movement position correction and finish buttons.In C, two lick events (L) and one movement event (A) are flagged, asindicated by the designations in the Event QC columns. In Panel D, thelick events have been excluded (indicated by “3” in the “Le” column),resulting in elimination of the movement event flag and the cageboundary violation.

FIGS. 17A-17-B shows a screen shot of a Stage 2 QC GUI showing anexample of a flagged failure-to-detect error. Panel A in the figureshows no licking events in the event plot except for one at the very endof the day while Panel B shows that the amount of water that the mouseconsumed that day (value highlighted within square) is similar to normaldaily intake for this and other mice (apx. 4 g) strongly suggesting afailure to detect licking events.

FIG. 18 is a flow diagram presenting certain operations employed in amethod of comparing behavioral patterns of a test and control groups inaccordance with various embodiments of the present invention.

FIG. 19 is a flow diagram presenting certain operations employed in amethod of selecting an optimal number of clusters to be used in acomparison clustering method in accordance with various embodiments ofthe present invention.

FIGS. 20A and 20B are diagrammatic representations computer systems thatcan be used with the methods and apparatus described herein.

FIG. 21A shows position probability density for a wild type (WT) mouseduring one day. The position probability density was calculated using akernel density estimator with a normal kernel function, a bandwidth of 1cm, and all positions of the mouse during a single day weighted by thetime spent at each position. For this mouse and day, the peak of themaximum position probability was 0.8 cm from the center of the observedlocation of the nest which was in the left rear of the cage at x=−13 cmand y=34 cm. In addition, smaller peaks were present in the left frontof the cage corresponding to the location of the feeder at x=−12.5 cmand y=−2.6 cm and in the right front of the cage corresponding to thelocation of the lick spout at x=0 cm and y=0 cm.

FIG. 21B shows the variation in position and occurrence of intake andmovement events for the same wild type mouse and day as in FIG. 21A.Circadian time is displayed on the x axis with the onset and offset ofthe dark cycle denoted by dashed lines at 12 and 24 hours. The positionof the mouse is displayed on the y axis as the distance from the tip ofthe lick spout whose xy coordinates were set to x=0 and y=0. Black linesindicate positions occurring during the inactive state and green linesindicate positions occurring during the active state. At the bottom ofthe plot, feeding events are displayed as orange rasters and drinkingevents are displayed as blue rasters.

FIG. 21C shows identification of inactive state pause threshold for thesame wild type mouse as in FIGS. 21A and 21C. Position durations for alldays are shown on the logarithmic x axis and the corresponding distancesfrom the longest position duration in a six hour window are displayed onthe y axis. The inactive state position duration threshold for thismouse was 9.3 minutes indicating the duration at which a rapid increasein the distance from the longest position duration was observed.Inactive state positions are displayed in black and active statepositions are displayed in green.

FIG. 21D shows the location of inactive state positions and intakeevents for the same wild type mouse and day. Inactive state positionsclassified using the position duration threshold cluster in the vicinityof the observed nest which is displayed as a small black box. For thismouse and day, the center of the inactive state positions was 0.3 cmfrom the center of the observed location of the nest. The dashed blacklines correspond to the floor of the cage and the solid black lines tothe lip of the cage. The feeder is represented by a small box at theleft front of the cage, and the water bottle is represented by a circleat the right front of the cage. The position of the mouse during feedingevents is displayed in orange and during drinking events in blue.

FIG. 22A relates to at device classification. A mixture of bivariatenormal distributions was fit to the positions of a WT mouse when it wasmaximally distant from the feeder during iei occurring during the lightcycle on all days. In the left hand panel, all positions assigned to thenine bivariate normal distributions in the final fit are displayed withdifferent colors and symbols. In the middle panel, only the centers ofthe bivariate normal distributions are displayed with the bivariatenormal distributions classified as occurring at the device displayed inorange and all other bivariate normal distributions displayed in green.In the right hand panel, the maximally distant IEI positions that wereclassified as occurring at the feeder are displayed in orange and allother positions are displayed in green indicating that locomotion awayfrom the feeder occurred during the WI. The right hand panel alsodisplays the location of the ramp that provided access to the feeder asa black rectangle protruding into the cage.

FIG. 22B shows results of IEI duration classification. A mixture ofunivariate normals was fit to the log transformed IEI durationsoccurring during the light cycle on all days for the same WT mouse. Thehistogram displays the square root of the number of IEI of a givenduration. The blue lines display the predicted individual univariatenormal distributions while the red line displays the predicted fit tothe data from the sum of the individual univariate normal distributions.The dashed line indicates the short IEI duration threshold for thismouse of 16 seconds for feeding occurring during the light cycle.

FIG. 22C shows results of IEI classification. The classification of alllight cycle IEI for this mouse as either WBI (within bout interval) orIBI (inter-bout interval) was determined from the mean of theprobabilities that an IEI maximum distance position occurred at thedevice and that the IEI duration was short. For each WI, the logtransformed duration is displayed on the x axis and the distance fromthe feeder is displayed on the y axis. Within bout IEI are orange.Interbout intervals are red if the mouse remained at the feeder but fora duration exceeding a short IEI threshold, green if the mouse engagedin locomotion, and blue if the mouse engaged in locomotion and drinking.

FIG. 23A shows a single light cycle active state. The left hand panelshows the distance of the mouse from the tip of the lick spout versuscircadian time. Green dots indicate positions occurring duringlocomotion bouts and red dots indicate positions moved to during feedingand drinking bouts or during bouts of other behavior. Red lines show thetime spent at a given position. At the bottom of the plot, verticalorange lines depict the onset and duration of feeding events, andvertical blue lines depict the onset and duration of drinking events.(At the time resolution depicted, most of the events are not resolvedinto individual lines but appear together). Above the lines depictingthe feeding and drinking events, thick orange or blue lines indicate theonset and offset of the feeding and drinking bouts.

FIG. 23B shows the location and duration of positions occupied by themouse during the active state depicted in FIG. 23A.

FIG. 23C shows the paths taken within the cage during the active statedepicted in FIG. 23A and TDE. Green symbols again indicate positionsoccurring during locomotion bouts and red symbols indicate positionsoccurring bouts of feeding, drinking, and other behaviors.

FIGS. 24A-C show daily amounts, intensities, and time budgets for WT andOB mice. FIG. 24A shows average daily intakes and movement. For eachgroup, the edges of the boxplots show the 75th and 25th percentilevalues, and the line within the box indicates the median. Data for eachmouse shown as dots. The OB mice exhibit a significant decrease inmovement (p=4.3×10⁻⁸: WT 471±37 m, OB 78±5 m) without significantchanges in food (p=0.047: WT 3.9±0.1 g, OB 4.16±0.08 g) or water (p=0.2:WT 3.3±0.1 g OB 3.6±0.1 g) intake.

FIG. 24B shows average daily bout intensity for feeding, drinking, andlocomotion. The OB mice exhibit a significant decrease in the intensityof locomotion bouts (p=7.9×10⁻¹¹: WT 13.3±0.4 cm/s, OB 5.0±0.2 cm/s)without significant changes in the intensity of feeding (p=0.6: WT0.78±0.03 mg/s, OB 0.76±0.03 mg/s) or drinking (p=0.03: WT 7.4±0.3 mg/s,OB 6.4±0.3 mg/s) bouts.

FIG. 24C shows average time budgets. The pie charts for each groupdisplay the division of time between the IS (black) and bouts of feeding(orange), drinking (blue), locomotion (green), and other behaviors(red). The OB mice demonstrate a significant increase in percent timespent in the IS (p=2.3×10⁻¹⁰: WT 66.8±0.9%, OB 83.5±0.5%) andsignificant decreases in the percent time spent in bouts of locomotion(p=2.4×10⁻⁵: WT 3.7±0.3%, OB 1.8±0.1%) and other behavior (p=3.7×10⁻¹¹:WT 22.4±0.7%, OB 7.4±0.4%). No significant changes in percent time spentin feeding (p=0.6: WT 6.4±0.4%, OB 6.7±0.2%) and drinking (p=0.08: WT0.63±0.03%, OB 0.71±0.03%) bouts. Bonferroni corrections were appliedfor multiple testing in evaluating the significance of amounts andintensities (3 tests: chow, water, movement) and time budgets (5 tests:inactive state, feeding, drinking, locomotion, and other bouts).

FIGS. 25A-C show daily amounts, intensities, and time budgets for WT and2C mice. FIG. 25A shows average daily intakes and movement. The 2C miceexhibit significant increases in movement (p=0.01: WT 515±50 m, 2C712±57 m) and food intake (p=0.007: WT 4.4±0.1 g, 2C 4.81±0.09 g)without a significant change in daily water intake (p=0.8: WT 3.6±0.1 g2C 3.6±0.1 g).

FIG. 25B shows average daily bout intensity for feeding, drinking, andlocomotion. The 2C mice exhibit a significant increase in the intensityof locomotion (p=0.006: WT 12.5±0.4 cm/s, 2C 13.9±0.3 cm/s) and feedingbouts (p=0.01: WT 0.98±0.07 mg/s, 2C 1.22±0.04 mg/s) without significantchanges in the intensity of drinking bouts (p=0.8: WT 7.6±0.3 mg/s, 2C7.7±0.4 mg/s).

FIG. 25C shows average time budgets. The 2C mice demonstrate asignificant decrease in percent time spent in the IS (p=4.8×10⁻⁵: WT66±1%, 2C 57±2%) and a significant increase in percent time spent inbouts of other behavior (p=9.2×10⁻⁷: WT 22.8±0.7%, 2C 32±1%). Nosignificant changes in the percent time spent in feeding (p=0.02: WT6.4±0.5%, 2C 5.0±0.2%), drinking (p=0.7: WT 0.61±0.02%, 2C 0.64±0.04%)or locomotion bouts (p=0.04: WT 4.4±0.3%, 2C 5.5±0.4%).

FIG. 26A displays the distance from the lick spout for a single day fora WT, OB, and a 2C mouse. Forest green lines indicate AS positions andblack lines indicate IS positions. At the bottom of the each plot,feeding (orange) and drinking (blue) events are displayed. FIG. 26Bdisplay eight days of data for the same three mice for feeding (orange),drinking (blue), and locomotion (neon green) events. ASs onsets andoffsets are indicated by open bars (forest green) above the events. FIG.26C displays all AS onsets and durations for the same days and mice asgreen dots. FIG. 26D displays all IS onsets and durations for the samedays and mice as black dots. For FIGS. 26C and 26D circadian time ofonset is on the x axis and the log duration is on the y axis. In orderto compare the pattern of state onsets and durations for each mouse withits group all state onsets and durations for 64 randomly selected mousedays in each group are displayed as grey dots in the background.

FIGS. 27A-27D show daily state patterns for WT and OB mice. Effects ofgenotype (G), time (T), and genotype by time interactions (G×T) weretested using 2×11 repeated measures ANOVA. In the upper right handcorner of each plot for this and subsequent figures, g indicates asignificant effect of genotype, t indicates a significant effect of timeof day, and x indicates a significant interaction of genotype with timeof day. For this and subsequent figures, if a significant genotype bytime interaction was present, post-hoc t-tests were carried out tocompare state properties for each time bin. An asterisk is displayed atthe center of each bin if a significant difference was detected(p<=0.05). Variation with time of day is displayed in 2 hour bins for WT(open squares) and OB (filled circles) mice: FIG. 27A shows ASProbability (G p=1.7×10⁻¹⁰, T p=8.2×10⁻⁶⁴, G×T p=2.0×10⁻²⁹); FIG. 27Bshows AS Onset Rate (G p=2.5×10⁻⁶, T p=7.8×10⁻¹³, G×T p=1.4×10⁻⁶); FIG.27C shows AS Duration (G p=0.96, T p=5.7×10⁻²⁵, G×T p=2.8×10⁻⁸) and FIG.27D shows IS duration (G p=5.7×10⁻⁸, T p=4.9×10⁻³², G×T p=1.0×10⁻⁵).

FIG. 27E shows comparison clustering plots. Comparison clusteringreveals a significant difference in the circadian time variation of ASnumber and duration between WT and OB mice (Σχ²=703, p<1.6×10−4). In theupper plot (WT) and lower plot (OB) each dot indicates the onset time (xaxis) and log duration (y axis) of an AS. Magenta dots indicate regionswhere the WT mice contribute significantly more active states than theOB mice. Grey dots indicate regions where the number of active statescontributed by the two groups is not significantly different. Theregions with significant differences account for 91.2% of the Σχ²indicating that these regions account for most of the difference in theAS patterns.

FIGS. 28A-28D show daily state patterns for WT and 2C mice. Variationwith time of day is displayed in 2 hour bins for WT (open squares) and2C (filled circles) mice: FIG. 28D shows AS Probability (G p=8.9×10⁻⁵, Tp=7.0×10⁻¹⁴⁸, G×T p=1.2×10⁻⁹); FIG. 28B shows AS Onset Rate (G p=0.002,T p=1.4×10⁻⁵², G×T p=4.4×10⁻¹³); FIG. 28C shows AS Duration (G p=0.5, Tp=1.8×10⁻⁴⁸, G×T p=1.2×10⁻⁶), and FIG. 28E shows IS duration (Gp=5.0×10⁻⁸, T p=p=9.6×10⁻⁸¹, G×T p=6.7×10⁻¹⁵).

FIG. 28E shows comparison clustering plots. Comparison clusteringreveals a significant difference in the circadian time variation of ASnumber and duration between WT and 2C mice (Σχ²=233, p=0.001). Cyan dotsindicate regions where the WT mice contribute significantly fewer activestates than the 2C mice. The regions with significant differencesaccount for 48.3% of the Σχ².

FIGS. 29A1-29A4 and 29B1-29B4 display plots showing feeding andlocomotion bout properties for WT and OB mice. The variation with timeof day are shown as follows: (FIG. 29A1) Chow intake (G p=0.1, Tp=5.7×10⁻³⁸, G×T p=3.4×10⁻⁸); (FIG. 29A2) Feeding bouts per hour (Gp=8.3×10⁻⁷, T p=2.5×10⁻²⁸, G×T p=2.8×10⁻¹⁹); (FIG. 29A3) Feeding boutsper active state hour (G p=4.4×10⁻⁵, T p=8.8×10⁻⁵, G×T p=0.2); (FIG.29A4) Feeding bout size (G p=1.4×10⁻⁵, T p=0.0004, G×T p=0.2); (FIG.29B1) Movement (G p=2.8×10⁻⁸, T p=6.3×10⁴⁷, G×T p=1.6×10⁻³⁶); (FIG.29B2) Locomotion 20 bouts per hour (G p=7.4×10⁻⁷, T p=1.4×10⁻⁴⁴, G×Tp=1.0×10⁻³²); (FIG. 29B3) Locomotion bouts per active state hour (Gp=1.8×10⁻⁶, T p=1.7×10⁻¹⁸, G×T p=3.3×10⁻¹³), (FIG. 29B4) Locomotion boutsize (G p=0.0167, T p=6.5×10⁻⁷, G×T p=0.06).

FIGS. 30A1-30A4 and 30B1-30B4 display plots showing feeding andlocomotion bout properties for WT and 2C mice. The variation with timeof day are shown as follows: (FIG. 30A1) Chow intake (G p=0.01, Tp=3.2×10⁻⁹², G×T p=4.9×10⁻⁹); (FIG. 30A2) Feeding bouts per hour (Gp=0.6, T p=6.1×10⁻⁴⁶, G×T p=0.001); (FIG. 30A3) Feeding bouts per activestate hour (G p=0.3, T p=8.2×10⁻¹⁰, G×T p=0.002); (FIG. 30A4) Feedingbout size (G p=0.4, T p=2.0×10⁻⁵⁴, G×T p=0.02); (FIG. 30B1) Movement (Gp=0.016, T p=6.8×10⁻¹⁰⁵, G×T p=3.8×10⁻⁵); (FIG. 30B2) Locomotion boutsper hour (G p=0.002, T p=9.2×10⁻¹⁰⁶, G×T p=1.3×10⁻⁸); (FIG. 30B3)Locomotion bouts per active state hour (G p=0.06, T p=4.2×10⁻⁵⁹, G×Tp=2.9×10⁻⁵); (FIG. 30B4) Locomotion bout size (G p=0.08, T p=4.2×10⁻¹⁸,G×T p=0.006).

FIGS. 31A-31F show plots related to the Within Active State Structurefor WT and OB mice. For WT (FIG. 31A) and OB (FIG. 31B) mice, the onsetsand offsets of feeding (orange), drinking, and locomotion eventsoccurring during 50 randomly selected ASs beginning and ending duringthe light cycle are displayed as open bars. Each line on the y axisdisplays the data for a single active state. Time during ASs is shown inminutes on the x axis with time zero indicating the onset of the ASs. InFIGS. 31C-31F, variation in bout probability with time since the onsetof the ASs for WT (open squares) and OB (filled circles) mice isdisplayed in one minute bins: (FIG. 31C) Feeding bouts (G p=4.6×10⁻⁶, Tp=3.3×10⁻⁶⁵, G×T p=6.2×10⁻⁴²); (FIG. 31D) Locomotion bouts (G p=0.2, Tp=6.2×10⁻¹⁸, G×T p=4.2×10⁻¹³); (FIG. 31E) Drinking bouts (G p=0.6, Tp=5.3×10⁻³, G×T p=0.0002); (FIG. 31F) Other bouts (G p=9.9×10⁻⁶, Tp=6.7×10⁻⁶³, G×T p=7.9×10⁻³⁴). Bonferroni corrections were applied formultiple testing in evaluating the significance of the boutprobabilities (4 tests: feeding, drinking, locomotion, and other).

FIGS. 32A-32F show plots related to the Within Active State Structurefor WT and 2C mice. For WT (FIG. 32A) and 2C (FIG. 32B) mice, the onsetsand offsets of feeding (orange), drinking (blue), and locomotion (green)events occurring during 50 randomly selected ASs beginning and endingduring the light cycle are displayed as open bars. For FIGS. 32C-32F,variation in bout probability with time since the onset of the ASs forWT (open squares) and 2C (filled circles) mice is displayed in oneminute bins: (FIG. 32C) Feeding bouts (G p=0.008, T p=4.9×10⁻¹⁵⁴, G×Tp=1.2×10⁻⁷), (FIG. 32D) Locomotion bouts (G p=0.6, T p=4.2×10⁻²², G×Tp=0.9), (FIG. 32E) Drinking bouts (G p=0.07, T p=7.1×10⁻⁶, G×T p=0.05),(FIG. 32F) Other bouts (G p=0.002, T p=3.5×10⁻¹³¹, G×T p=0.0001).

FIG. 33 shows the classification of short and long duration partitions.The mean durations and at device probability for all IEI partitions forall WT mice from the WT2C comparison are displayed. The IEI are forphotobeam event data occurring during the light cycle. Short durationpartitions were identified by fitting a line to the data using localinterpolation (lowess smoother) in order to estimate the mean partitionduration at which mice in this group where equally like to remain at orleave the feeder.

FIGS. 34A-34D shows locomotion bout classification plots. Theprobability density estimates for a single mouse for movement rate (FIG.34A) and turning angle (FIG. 34B) are displayed. The densities formovement events in the training set (MIP) are shown in red. Thedensities for movements to be classified (M_(AS∉IB)) are shown in green.The dashed lines indicate the intersection of the MIP and M_(As∉IB)densities. The relative probabilities that a movement rate (FIG. 34C) orturning angle (FIG. 34D) of the M_(AS∉IB) positions were distinct fromthe MIP positions are plotted versus movement rates or turning angles.The probability density for movement rate and turning angle wasestimated using a kernel density estimator with a normal kernel functionfor movements occurring during inactive states and bouts of feeding ordrinking (red) and for all other movements (green). The intersection ofthe two probability densities was set as the threshold for classifying amovement as occurring within a locomotion bout or during a bout of otherbehavior (stop moving in place).

FIGS. 35A-35B shows plots related to cluster number selection in amethod of comparison clustering. The log p values calculated from thechi square distribution for the delta chi square sums are plotted versusthe number of clusters for the WTOB comparison (FIG. 35A) and the WT2Ccomparison (FIG. 35B). The dashed line shows the location of the minimump value and the dotted lines show the range over which the p values arenot significantly different from the minimum p value. The number of binsselected is 13 for the WTOB comparison and 14 for the WT2C comparison.

FIGS. 36A-36D shows active state amounts for WT and OB mice. Thevariation with time of day is shown as follows: (FIG. 36A) AS Duration(G p=0.96, T p=5.7×10⁻²⁵, G×T p=2.8×10⁻⁸), (FIG. 36B) AS Chow (Gp=5.6×10⁻⁶, T p=2.8×10⁻²¹, G×T p=5.57×10⁻⁵), (FIG. 36C) AS Water (Gp=7.4×10⁻⁶, T p=9.6×10⁻³⁶, G×T p=4.6×10⁻⁹), (FIG. 36D) AS Movement (Gp=7.3×10⁻⁵, T p=1.7×10⁻²⁰, G×T p=4.2×10⁻¹⁴).

FIGS. 37A-37D shows active state amounts for WT and 2C mice. Thevariation with time of day is shown as follows: (FIG. 37A) AS Duration(G p=0.5, T p=1.5×10−48, G×T p=1.2×10−6), (FIG. 37B) AS Chow (G p=0.8, Tp=1.0×10−43, G×T p=7.2×10−11), (FIG. 37C) AS Water (G p=0.07, Tp=1.5×10−50, G×T p=5.1×10−8), (FIG. 37D) AS Movement (G p=0.7, Tp=5.7×10−43, G×T p=0.003).

FIGS. 38A1-38B4 shows drinking and “other” bout properties for WT and OBmice. The variation with time of day is shown as follows (FIG. 38A1)Water intake (G p=0.09, T p=9.6×10⁻⁵³, G×T p=7.5×10⁻⁷), (FIG. 38A2)Drinking bouts per hour (G p=0.0006, T p=2.1×10⁻⁵⁰, G×T p=2.9×10⁻⁶).(FIG. 38A3) Drinking bouts per active state hour (G p=0.5, Tp=3.6×10⁻¹², G×T p=9.5×10⁻⁷), (FIG. 38A4) Drinking bout size (G p=0.003,T p=0.002, G×T p=0.005), (FIG. 38B1) Other time (G p=2.2×10⁻¹¹, Tp=3.3×10⁻⁶², G×T p=3.5×10⁻⁴⁰). (FIG. 38B2) Other bouts per hour (Gp=2.9×10⁻⁷, T p=1.6×10⁻⁴⁷, G×T p=1.4×10⁻³⁴). (FIG. 38B3) Other bouts peractive state hour (G p=5.6×10⁻⁷, T p=8.510⁻¹⁸, G×T p=2.5×10⁻¹¹), (FIG.38B4) Other bout duration (G p=0.003, T p=9.3×10⁻⁹, G×T p=0.06).Bonferroni corrections were applied for multiple testing in evaluatingthe significance of water intake (3 tests: chow, water, movement), timespent in other bouts (5 tests: inactive, feeding, drinking, locomotion,other), bout rates (5 tests: inactive state rate, feeding, drinking,locomotion, and other), water bout size (3 tests: feeding, drinking,locomotion), and other bout duration (5 tests: inactive state, feeding,drinking, locomotion, and other).

FIGS. 39A1-39B4 shows drinking and “other” bout properties for WT and 2Cmice The variation with time of day is shown as follows: (FIG. 39A1)Water intake (G p=0.8, T p=5.5×10⁻¹⁰¹, G×T p=5.2×10⁻⁹), (FIG. 39A2)Drinking bouts per hour (G p=0.04, T p=5.1×10⁻⁷³, G×T p=0.1), (FIG.39A3) Drinking bouts per active state hour (G p=0.9, T p=3.9×10⁻²⁷, G×Tp=0.003), (FIG. 39A4) Drinking bout size (G p=0.007, T p=9.4×10⁻¹⁷, G×Tp=0.9), (FIG. 39B1) Other duration (G p=1.6×10⁻⁶, T p=6.2×10⁻¹⁴⁶, G×Tp=2.0×10⁻¹²), (FIG. 39B2) Other bouts per hour (G p=0.002, Tp=7.5×10⁻¹⁰⁸, G×T p=3.4×10⁻⁸), (FIG. 39B3) Other bouts per active statehour (G p=0.06, T p=8.9×10⁻⁵⁴, G×T p=4.8×10⁻⁵), (FIG. 39B4) Other boutduration (G p=0.5, T p=8.9×10⁻²³, G×T p=0.2).

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 1. Introduction andRelevant Terminology

The present invention relates to methods, systems and apparatus for thecollection, management, and analysis of high-resolution behavioral data.These systems and methods provide an opportunity to examine behavioralpatterns with levels of precision and quantization that have not beenpreviously achieved. Methods and systems for managing and analyzing thevery large and unique datasets produced by behavioral monitoringsystems, including quality assessment and control, archiving, dataquery, data reduction, analytical procedures and visualizationtechniques are provided. Such detailed analyses of spontaneous behaviorprovide fundamental insights into the neural organization of behaviorand enable detection of genetic, pharmacological and environmentalinfluences on brain function with high sensitivity.

While much of the description below is presented in terms of systems,methods and apparatuses that relate to behavior of animal subjects inhome cage monitoring (HCM) systems, the invention is by no means solimited. For example, the methods and systems for filtering andanalyzing behavioral data may be used with any behavioral monitoringsystem. In the following description, numerous specific details are setforth in order to provide a thorough understanding of the presentinvention. It will be apparent, however, that the present invention maybe practiced without limitation to some of the specific detailspresented herein.

The invention relates to the filtering, data quality control andassessments, and analysis of data from behavioral monitoring systems. Ingeneral, the behavioral monitoring systems include one or more devicesin a defined area, at or with which the animal subject(s) beingmonitored interact. The monitoring system may be a home cage monitoringsystem such as described in U.S. Pat. No. 7,086,350, titled “Animal CageBehavior System,” incorporated herein by reference in its entirety forall purposes. Typically, the monitoring system provides continuousmonitoring of movement and device event data over a measurement period.For example, the monitoring system may provide data resulting fromcontinuous monitoring of movement (e.g., in the form of spatial positionversus time), as well as ingestive events, sensory stimuli events, etc.

The behavioral monitoring systems used in accordance with the methodsand systems of the invention produce large volumes of data, e.g., asingle subject over a day may produce tens to hundreds of thousands ofmovements, thousands to tens of thousands of ingestive events, etc.Multiplying this data by hundreds or thousands of subjects over weeks,years, etc. of observation requires techniques for robust automatedquality assessment and correction of data. Methods and systems of dataquality assessment and control are discussed below.

Another aspect of the invention are novel quantitative approaches fordefining elements of behavior and their temporal and spatialorganization, including data reduction, visualization and analysismethods that are the most biologically relevant. These approaches arefacilitated by the data quality control algorithms. In particular,classification of clusters and bouts of behavior, as well as theclassification of active and inactive states of behavior are describedbelow. In certain embodiments, methods and systems are provided thatallow behavioral classification to be performed in robust, automatedfashion.

The following terms are used throughout the specification. Thedescriptions are provided to assist in understanding the specification,but do not necessarily limit the scope of the invention.

A behavioral event is an instance or occurrence of a particular type ofbehavior. Examples of types of behavioral events include events relatedto consumption behavior, (including consumption of food, liquid,medicines, pharmaceuticals, etc.), events related to movement behavior,events related to communication, events related to various commonactivities associated with the subject being monitored. For example,behavioral events that may be measured for a mouse in a cage includefeeding, drinking and movement about the cage. Behavioral events thatmay be measured for a human include feeding, drinking, movement around acertain area, and using a particular electronic device such as a phoneor computer, etc. Other behavioral events may relate to animal responsesto particular stimuli or devices

A device event is a behavioral event that involves interaction with adevice at a known location. The location may be fixed or variable.Examples include feeding events, which occur at a feeder in a cage andlick events, which occur at lickometer in a cage. Other examples of adevice event include use of a computer at a known location within ahouse, feeding events that occur at a particular restaurant as indicatedby interaction with a device at that restaurant.

Examples of devices include a lickometer, a device that provides ameasure of fluid consumption by an animal, and a feeder, a device thatprovides food to an animal in captivity. In certain embodiments, thefeeder provides a measure of the amount of food consumed by the animal.Interaction with the device may be an interaction with the devicenecessary to the behavior being measured. For example, water consumptionby a mouse may be measured at a lickometer by a change in capacitance inthe licking spout when licked by the mouse to obtain water. Similarly,feeding may be measured by a photobeam and photobeam detector when ananimal breaks a photobeam in order to reach food in a feeder. Otherdevices include running wheels, levers and holes. Levers and holes maybe interacted with for delivery or provision of food, fluid, drugs, orany sensory stimulus. In certain embodiments, the device is an operantconditioning device. Interaction with a device may involve exposure toanother animal, sensory stimuli (e.g., odorant) or a novel or familiarobject, with the measurement providing behavioral information about theanimal's subject response to the exposure or sensory stimuli, etc.

An inter-event interval is the interval between two behavioral events ofthe same type: for example, the interval between two photobeam breaks.Similarly if a certain behavior is measured by interaction with acomputer, an inter-event interval may be the interval between keyboardkeys being pressed, between mouse clicks, etc.

An event onset error refers to an erroneous measurement of the onset ofan event when no device event in fact occurred. Jostling of a cage,brief occlusion of a photobeam by shifting chow or electromagnetic fieldnoise detection by a lickometer are examples of sources of occasionalspurious feeding and drinking event measurements.

An event termination error refers to an erroneous measurement thatindicates that a device event is ongoing when it has in fact terminated.Examples of sources of event offset errors include feeder photobeamsbecoming blocked by food particles during a feeding bout. Lickometerfailure could result from spontaneous dripping, or placement (by themouse) of bedding material in the lick slot. Such errors, if undetected,would produce overestimates of device event length and an erroneousindication of prolonged activity by the animal at the device.

Movement data includes information about the movement of an animalsubject in the measurement area. It may include spatial and temporalinformation, e.g., the spatial position of the animal at times duringthe measurement period. Movement data may also be collected at certaintimes, e.g., 1 second, though in many embodiments to reduce the amountof data in a raw data set, movement data may be collected when theanimal moves more than a threshold amount. Data collection thresholddistances vary according to the behavioral monitoring system and type ofsubject: for human subjects in a large measurement area, thresholds onthe order of kilometers may be appropriate, for other animals, metersmay be appropriate, for rodents centimeters, etc. Movement data may thusinclude the animal's positions and the time of each position, or theduration since the previous position. Position and/or movement may bemeasured by any number of mechanisms, including load beams, RFIDtransponders, satellite systems, video tracking, etc.

Drift refers to accumulated error associated with a measurement. Overallposition drift is the drift in x and y coordinates (and/or othercoordinates or dimensions if measured) in the measurement area at anytime during a measurement period. For example, where load beams are usedto monitor animal movement, movement measurements are influenced bychanges in the distribution of mass within the cage. Changes may occurin the animal's body weight, in the amount of food in the feeder andwater in the lickometer, as well as by shifting of bedding material. Ashift of position information in the y axis, such that the locomotorpath and the ingestive behavior locations shift up relative to the cagelocation may result from the removal of food and water from devices atthe opposite end of the cage. Device event drift is the apparent driftin the location of a device as measured at each event. As with overallposition drift, the device event drift is typically measured for eachcoordinate or dimension.

The animal subject(s) behavior can be broken down into bouts andclusters. Bouts are the occurrence or repeated occurrences of the samebehavioral act or indication of a behavioral act (e.g., food consumptionor photobeam breaks) that appear to cluster together in time and/or arenot separated by the intervention of a different behavior. In certainembodiments, a bout may be characterized by the occurrence and/orrepetition of a behavior at a particular location. Clusters are repeatedbouts of the same behavioral act or indication of a behavioral act(e.g., food consumption or photobeam breaks) that appear to clustertogether in time.

The animal subject(s) behavior may be further organized into states,e.g., active and inactive states. A state may be characterized byincreased probability of a particular behavior or behaviors and/or theoccurrence of these behaviors at one or more characteristic locations.For example, active states and inactive states may be classified. Activestates are states in which there is an increased probability of somemeasured behaviors (such as feeding, drinking, or locomotion) occurring.Inactive states are states in which the probability of being incharacteristic location or locations is high over some measurementwindow. These characteristic locations may act as refuge from predationor environmental conditions. During inactive states, the animalsubject(s) may have an increased probability of engaging in certainmeasured behaviors (such as rest or sleep). Although for the sake ofdiscussion, the below description chiefly refers to active/inactivestate classification, the methods are not so limited and may be used butmay used to identify and classify other states in which there is anincreased probability of a particular behavior or behaviors occurring ata particular location or locations.

Embodiments of the present invention relate to tangible and intangiblecomputer readable media or computer program products that includeprogram instructions and/or data (including data structures) forperforming various computer-implemented operations. Computer readablemedia or computer program products that include program instructionsand/or data (including data structures) for performing variouscomputer-implemented operations. Examples of computer-readable mediainclude, but are not limited to, magnetic media such as hard disks,floppy disks, magnetic tape; optical media such as CD-ROM devices andholographic devices; magneto-optical media; semiconductor memorydevices, and hardware devices that are specially configured to store andperform program instructions, such as read-only memory devices (ROM) andrandom access memory (RAM), and sometimes application-specificintegrated circuits (ASICs), programmable logic devices (PLDs) andsignal transmission media for delivering computer-readable instructions,such as local area networks, wide area networks, and the Internet. Thedata and program instructions of this invention may also be embodied ona carrier wave or other transport medium (e.g., optical lines,electrical lines, and/or airwaves).

Database refers to a means for recording and retrieving information. Thedatabase may also provide means for sorting and/or searching the storedinformation. The database can include any convenient media including,but not limited to, paper systems, card systems, mechanical systems,electronic systems, optical systems, magnetic systems or combinationsthereof. In certain embodiments databases include electronic (e.g.computer-based) databases. Computer systems for use in storage andmanipulation of databases are well known to those of skill in the artand include, but are not limited to “personal computer systems”,mainframe systems, distributed nodes on an inter- or intra-net, data ordatabases stored in specialized hardware (e.g. in microchips), and thelike.

2. Data Quality Control

Behavioral monitoring systems generate large volumes of data. Forexample, a 32 cage monitoring system for mice, each cage containing afood consumption indicator, a fluid consumption indicator an activityplatform to measure movement, has 96 data collection devices. Per day,each device may record thousands of events, e.g., 500-5,000 feedingevents, 1,000-10,000 lickometer events as well as 10,000-350,000 spatialpositions. Robust automated quality assessment algorithms are needed toprocess these events. The effective use of large biological datasetsrequires novel methods for assessing data quality. Data quality can becompromised, for example, by mechanical failure or by idiosyncraticinteractions of animal subjects with devices. Assessment of the qualityof behavioral system data requires careful consideration of numerousfactors that can compromise the quality of behavioral data.

Aspects of the present invention relate to quality control andassessment of large volumes of data generated by a behavioral monitoringsystem. According to various embodiments, the methods incorporateexperimenter observations relating to periodic intake measurements,animal subject and device appearance, and environmental conditions. Inaddition, automated techniques are required to monitor the function ofbehavioral data collection devices are presented.

Behavioral monitoring systems that assess feeding, drinking andlocomotor activity continuously, with high temporal and spatialresolution. The high resolution of the collected data is critical forthe development of analytical approaches that discriminate behavioralpatterns with high sensitivity. However, the complex nature and largesize of these behavioral datasets pose multiple challenges. Theseinclude: 1) the requirement for a high-volume behavioral data managementsystem for data storage and querying, 2) the development of qualitycontrol tools to detect and manage episodes of system noise, devicefailure and human error, and 3) the development of data reduction andanalysis techniques to maximize the ability to detect genetic or otherinfluences on behavioral patterns. Because these datasets are unique,novel solutions are required to meet these challenges.

As indicated above, the datasets typically contain data relating tomovement or spatial position of an animal in the measurement area, aswell as behavioral device event data from one, and more typically,multiple devices. According to various embodiments, the quality controlmethods involve analyzing the behavioral data to detect inconsistenciesbetween the position information and device information and/or betweeninformation received from multiple devices.

FIG. 1 shows overviews of a process of filtering data according tocertain embodiments, with FIGS. 2-6 showing details of specificembodiments of certain operations described in FIG. 1. Some or all ofthe operations described in the FIG. 1 may be used for data qualityassessment and producing a filtered dataset to analyze. Additionalquality control operations may also be performed. The process beginswith receiving movement and device event data for a measurement period(101). The combined movement and device event data for a measurementperiod and measurement area (e.g., a cage) may be referred to as adataset. The datasets may take any form, and may include data formultiple animal subjects, etc. In many embodiments, movement data ispresented as position versus time data. Device event data may includeindications of interaction with the device at various times during themeasurement period. For example, position/locomotion data in the datasetmay include a record of every time the animal moved a distance greaterthan a certain predetermined distance (e.g., 1 cm, 10 feet, etc.). Thedata may be in the form of, e.g., the animal location and the timeduring the measurement period or the duration from the previous recordedlocation. Similarly, for drinking or eating behavior, a dataset mayinclude the time of duration between signals from the food and fluidconsumption devices. Data may be collected, e.g., using methodsdescribed in above-referenced U.S. Pat. No. 7,086,350, referenced above,received from external sources, etc.

The process continues by identifying and removing false device eventdata (103). This involves detecting event onset errors, also referred toas false device event onsets. Sources of spurious device events, such asjostling of the cage, brief occlusion of a photobeam by shifting chow,electromagnetic field noise detection by the lickometer may occasionallyproduce spurious feeding and drinking events. Similarly, in a behavioralmonitoring system that relies on a subject to press a button on a mobilephone or tracker in a deliberate manner, a false device event onset mayoccur by inadvertent interaction. Any type of error that results in anindication of a device event when in fact no device event occurred is anevent onset error. Although these events are typically infrequent, theirsignificance is enhanced in embodiments in which device events are usedfor movement position correction (described below).

After detecting event onset errors and removing the associated deviceevent data from the data set, corrections to the position movement dataare calculated (103). In certain embodiments, inaccuracies in positioninformation may accumulate. For example, where load beams are used tomonitor animal movement, movement measurements are influenced by changesin the distribution of mass within the cage. Changes may occur in theanimal's body weight, in the amount of food in the feeder and water inthe lickometer, as well as by shifting of bedding material. If suchchanges are not accounted for, then inaccuracies in position informationresult. An example of movement position error is shown in FIG. 2, whichdepicts a 24-hr behavioral record in which locomotor positions areindicated in green, feeding event locations in orange, and drinkingevent locations in blue. Here, at 201, we see evidence of a shift ofposition information in the Y axis, such that the locomotor path and theingestive behavior locations shift backward (up), relative to the cagelocation. Such inaccuracies result from the removal of food and waterfrom devices at the front of the cage.

FIG. 2 shows an example of inaccuracies that can result using a loadbeam in a cage to measure position information. Regardless of theposition detection mechanism (load beam, video, RFID, etc.) and themeasurement area, position information in the dataset may containinaccuracies. The device events (e.g., drinking and feeder photobeambreaks) occur at known locations in the cage or other measurement area,and this information is used to correct movement and position data. Thecorrected behavioral record is depicted in FIG. 2 at 203. Details of oneembodiment of correcting movement position data are described furtherbelow with respect to FIG. 6. It should be noted that because the knownlocations of the devices (and the expected positions of the animal) areused to correct inaccuracies in position data, it is optimal to removedevice event data associated with false device event onsets as describedabove prior to correcting the position data. Movement positioncorrection may also be performed prior to the removing false deviceevents in addition to after their removal.

Once the position data are corrected, inaccuracies in the datasetresulting from failure of a device event to terminate are identified andremoved (109). In some cases, an animal may initiate a device event thatdoes not terminate when the animal leaves the device. For example, afeeder photobeam may become blocked by food particles during a feedingbout. Lickometer failure could result from spontaneous dripping, orplacement (by the mouse) of bedding material in the lick slot.Similarly, video, satellite or electronic tracking could malfunctionfailing to register termination of a device event. Such errors, ifundetected, would produce overestimates of device event length and anerroneous indication of prolonged activity by the animal at the device.In certain embodiments, failure of a device to terminate is detected byfinding all positions of the subjects during device events, using datain which device onset errors have already been detected and excluded,and the position corrections have been performed. The positions of thesubjects during device events are clustered using a rapid nearestneighbor clustering algorithm. If there are no event termination errors,then there should only be one cluster centered at the device. If morethan one cluster is present, the largest cluster centered closest to thedevice is considered to contain valid events, and events occurringelsewhere are excluded. Further details of detecting device eventtermination failure according to certain embodiments are discussed belowwith reference to FIG. 7.

In certain embodiments, the overall measured movement of the animalduring the measurement period is compared to known animal behavior todetect potential errors with the position detection mechanism (113). Forexample, a load beam can malfunction, producing errors in positionmeasurements. In one embodiment, a screening strategy for instances ofmalfunction makes use of the predisposition of animals to explore theentire area of their cage enclosures during the course of a 24 hourrecording period. Saturation of a load beam results in truncation orskewing of the movement data and problems such as loosening of thecentral pivot can also result in underestimates of force that result intruncation of movement data. Further details of detecting load beam orother position detector malfunction according to certain embodiments arediscussed below with respect to FIG. 10. Similarly, position data can becompared to known animal behavior for other types of position detectionmechanisms. In addition, the position data can be examined for boundaryviolations, i.e., positions outside the measurement area, where thesubject is incapable of going.

Various embodiments of the operations in FIG. 1 are discussed below.

A. Detecting Device Onset Errors

Detecting potential device event onset errors involves detectinginconsistencies between recorded device events and independentlygathered position data. For example, signals from a lickometer mayindicate that a lick event occurred at a certain time when position dataat that time indicates that the mouse or other subject is not at thedevice at that time. In such cases, errors exist in either device datacollection or movement data collection. In certain embodiments, themethods of the invention use position information to detect and flagpotentially erroneous device events for removal and/or subsequent userreview.

In certain embodiments, once an inconsistency between position data ofthe animal and device onset data is identified, a determination is madewhether the error is from device data collection or movement datacollection. If it is determined that the error is from the device datacollection, that device event may be removed automatically or flaggedand presented to a user for a decision on whether to remove it.

As discussed above, a potential event onset error is detected when theindication of a device event at a particular time is inconsistent withthe measured position of an animal at that time. In certain measurementsystems, position (movement) data is collected by mechanisms for whichaccumulated error can be a problem. This accumulated error is referredto as drift. If device events occur when position data indicates thatthe animal is not near the device, then either a large position drift ora false event onset has occurred. These possibilities can bedistinguished by measuring the overall position drift and comparing thiswith the drift in the indicated positions of the animal during deviceactivations. If the overall position drift at the time of an eventactivation is similar to the drift in the position of the animal at theonset of a device activation, then the event would be considered valid.A difference in the overall drift and the device drift raises thepossibility of a spurious device event. FIG. 2 is a process flow sheetsshowing an overview of a process of detecting event onset errors bycomparing device event drift with overall position drift according tocertain embodiments.

The process begins by measuring the overall position drift (PD) for ananimal subject in a time period (301). (Measuring the overall positiondrift is discussed below with reference to FIG. 4). Each device event isthen considered. For a given device event, the indicated or measuredposition of the animal subject at the time t of the event onset isobtained (303). The device event drift (ED) is then determined (305).The difference between the position drift at time t (PD_(t)) and thedevice event drift (ED) is determined (307). This difference is comparedto a threshold difference (309); if the difference is larger than thethreshold, the event is flagged to be presented to a user (311).Alternatively, the device event may be automatically classified as falseand the associated data removed. There is a check for remaining deviceevents at decision block 313. If there are remaining device events forthe subject and measurement period, the flagged events are presented tothe user for review (315).

Calculating the ED involves comparing the measured position of theanimal at the time of an event with the first measured of position ofthe event in the measurement window. This will be the expected positionof the animal during an event based on the known location of the deviceif the first event of this type was used to initialize the coordinatesof the measurement area. Initialization of the coordinates is describedas part of movement position correction described below.

To obtain an estimate of the overall position drift, an estimate of thedrift in the minimum and maximum positions (X, Y, and/or others) thatdefine the boundaries of the subject's movement in the measurement area,is obtained. In certain embodiments, determining overall position driftinvolves fitting a convex hull to the X and Y (or other coordinate)positions vs. time in a sliding time window with the requirement thatthe distance from the minimum to the maximum position on each side ofthe convex hull must be greater than a certain percentage of the width(X positions) or length (Y positions) of the cage or other measurementarea. Any type of coordinate system appropriate for the particularmeasurement area may be used. The overlapping convex hulls areindependently expanded until the distance requirement is met, yieldingan estimate of the overall drift in the minimum and maximum positions.This position envelope can then be averaged, and the average used toobtain an estimate of the overall drift at any time during datacollection. If this drift differs from the apparent drift in deviceposition by more than a certain threshold, e.g., 10 cm in X or 15 cm inY, then the device event is flagged for subsequent review as inoperation 311.

FIG. 4 is a process flow sheet showing key operations in obtaining theoverall position drift as described above. The process begins byreceiving position vs. time data for the subject and measurement period(401). For a position coordinate, e.g., X, Y and/or any othercoordinate, a convex hull is fitted to the coordinate positions vs. timefor a window of duration d and initialized at time t₀ (403). The convexhull is expanded in time to a time t_(n) at which the convex hullencompasses a predetermined number of coordinate position units, x(405). For example, the convex hull may be expanded until the convexhull encompasses a certain percentage, e.g., 80%, of the total width orlength, etc. of the measurement area. Operations 403 and 405 are thenrepeated for a convex hull of duration d, initialized at time t_(n) andexpanded until the distance requirement is met at time t_(m) (407). Thisis repeated until t_(m) is the measurement or observation time period,e.g., 1 day. Position drift for the coordinate is then estimated byobtaining the mean of the max and min positions along the convex hullsat any time, thereby obtaining position drift as a function of time. Theprocess described in FIG. 4 is performed for each position coordinate,yielding for example a position drift at time t=3 hours of −3 cm in theX-direction and 2 cm in the Y-direction.

A graphical example of the detection of event onset failures is shown inFIG. 5A. These graphs plot differences between the overall positiondrift and lickometer event drifts (blue) and photobeam event drifts(red) over a 24-hr monitoring period. X and Y axis drifts are plottedseparately. The drift difference thresholds for flagging events, in thiscase 10 cm in X and 15 cm in Y, are indicated by dashed lines. In thisexample, two instances are flagged in which Y axis drift differencesexceed threshold for lickometer events. The lickometer event data can beautomatically excluded or the flagged events can be presented to theuser for review, as in FIG. 5B.

B. Movement Position Correction (MPC)

Movement position correction uses the known locations of device eventsto correct movement and position data. The MPC algorithm compares theanimal's position at each device event onset, as calculated from themovement/position data, with the expected position of the animal, basedon the known location of the device. If the calculated and expectedpositions differ by more than a threshold amount, movement data in theprior loop are corrected.

Certain operations are illustrated in the process flow sheet of FIG. 6.The process begins by initializing coordinates (601). At the beginningof each session (measurement period), the animal subject's coordinatesare initialized. This initialization can take place at the first deviceevent with the animal subject's coordinates initialized based on theexpected position of the animal subject at the device as in the exampleshown in FIG. 6, though any appropriate initialization may be used. Thepositions of the animal prior to the first device event (or otherinitialization) can then be back-calculated. At the next device event(DE_(n)), the position of the animal subject as measured by the loadbeam, video tracking, other position detection mechanism, etc. iscompared with the expected position of the animal subject based on theknown location of the device (603). This difference is the positiondrift (PD). This comparison is done for each position dimension (X and Yin the example.) If the difference between measured and expectedpositions during the device event exceeds a certain threshold (seedecision block 605), the position data in the dataset is corrected bydistributing that difference across the measured positions betweenDE_(n) and the previous device event (607). The distribution may beweighted by the distance moved between positions. This process isrepeated for the next device event DE_(n+1) (609) until all deviceevents in the measurement period are considered.

The use of the MPC tool in correcting movement and position data for amouse in a cage is shown in FIG. 2, discussed above. However, the MPCtool may be used for a variety of experimental settings in whichinteractions of animal subjects with any device having a known locationare available to validate and correct position information. In FIG. 1,the MPC tool is shown as being performed after detection and removal offalse device onsets: this can be important as the MPC relies on expectedpositions of the animal subjects to correct position information. Incertain embodiments, the MPC tool may be run prior to removing falsedevice events, and rerun after they are removed.

C. Detecting Device Event Termination Failure

As indicated above, in certain embodiments, detecting instances of adevice failing to terminate involves using a nearest neighbor clusteringalgorithm. All positions of the animal subject during device events areclustered. FIG. 7 shows key operations in a process flow sheet: theprocess begins by receiving all position data for all device events fora particular device (701). As indicated above, this is data for whichdevice onset errors have already been detected and excluded, and the MPCtool has been run or rerun. For each device event, the maximum positionfrom the starting position for that event is obtained (703). A clusteranalysis is then performed to cluster these maximum positions (705). Ifthere are no event termination errors, then there should only be onecluster centered at the expected device position. If more than onecluster is present, the largest cluster centered closest to the expecteddevice position is considered to contain valid events, and eventsoccurring elsewhere are excluded. Thus, the cluster closest to theexpected device position is accepted (707). All events having maximumpositions outside of the accepted cluster are removed (709). One ofskill in the art will understand that different clustering and exclusioncriteria may be used. It should also be noted that the event data may beautomatically removed, or flagged and presented to a user for a decisionon removal.

A graphical example of clustering feeding and drinking events in a mousehome cage monitoring system is shown in FIG. 8. In the example shown inFIG. 8, a cluster of feeding events near the feeder (green squares) anda cluster of drinking events near the lickometer (blue circles) havebeen identified. The two squares in the opposite corner represent themaximum distances of the animal from the feeder determined during twofeeding events. The red coloring indicates that they fall outside thecriteria for valid feeding events.

D. Detection of Position Detector Malfunction

In certain embodiments, a behavioral monitoring system utilizes loadbeams to function as force transducers for determination of animalmovement and position. Occasionally, a load beam can malfunction,producing errors in these measurements. Saturation of a load beamresults in truncation or skewing of the movement data and problems suchas loosening of the central pivot can also result in underestimates offorce that result in truncation of movement data. An example of data inwhich such error has occurred in graphically shown in FIG. 9, with themeasured positions in green. The potential for these types of errorslies not just with load beams, but other types of position detection.For example, position detectors that rely on mobile tracking devices inlarge area measurement areas such as cities, etc., may losereception/transmission in certain geographic areas due to weather, etc.

In certain embodiments, detection of such errors involves comparing allcorrected movement positions during the measurement period to known orexpected animal behavioral patterns. One example is the predilection ofa mouse to explore its entire cage area over the course of a 24 hourmeasurement period. Another example is the expectation or a preferencefor a human subject to roam an area located next to a workplace duringthe course of a day or week.

In certain embodiments, detection of such errors involves plotting theconvex hull of all corrected movement positions and comparing thatconvex hull to known or expected animal behavioral patterns. Forexample, the comparison may involve calculating the percentage of themeasurement area that the convex hull occupies. If less than a certainpercentage, e.g., 80%, of the cage area is occupied by the convex hull,then the data from the day or other measurement period of data may beflagged for subsequent user review. FIG. 10 is a process flow sheetshowing key operations in one embodiment of detection position detectormalfunction from position data received for a measurement period. Theprocess begins by generating a convex hull of the measurement area todefine a measurement area footprint (1001). Other methods to generate orpre-existing knowledge of the measurement area footprint may be used. Aconvex hull of all measured positions in the measurement period isgenerated (1003), and the percent intersection of the convex hulls iscalculated (1005). The intersection is compared to a threshold indecision block 1007: if it greater than the threshold, the data isaccepted, or at least not flagged (1011). If it is less than thethreshold, a determination is made whether to remove the data or not(1009). The determination can be made after user review, or in otherembodiments, data can be automatically removed.

In certain embodiments, the comparison of the measured positions withthe measurement area footprint may involve analyzing overlap in specificareas of the measurement area, e.g., a northeast quadrant of a city,etc. Note that other types of position error detection may also beemployed, including detecting boundary violations. Comparison ofmeasurement area footprint with measured positions may reveal systematicmalfunction with the position detector, such as load beam saturation, asopposed to isolated errors such as stray signals picked up from outsidethe measurement area.

E. Computer Implemented Methods of Automated and User Data QualityControl

As described above, the data quality control algorithms may involve someuser review combined with automated algorithms. For example, dataquality control determinations resulting from user entered comments orautomated algorithms described above can have a three level structure inwhich each event is assigned a quality of 1 (use), 2 (flag for furtherinspection), or 3 (don't use). A quality of 2 indicates the existence ofa potential error that must be inspected by the investigator. Tofacilitate this inspection, tools are provided that will allow theexperimenter to view and process these potential errors. Datavisualization techniques facilitate examination of the data and theflagged errors, allowing the investigator to determine whether each flagwarrants a downgrade to exclusionary status or an upgrade to “ok to use”status.

In certain embodiments, a quality control process can be performed intwo main stages (Stage 1 and Stage 2). In Stage 1, automatic algorithmsare run to search, e.g., for cumulative errors in position data,position detector failure, false device event onsets. Potential errorscan be flagged for further inspection. The experimenter will thenprocess all the flagged errors using a graphical user interface (GUI).Once this is done, all movement data will be either excluded orcorrected. This fully processed movement data will then be run through asecond stage, where a device termination algorithm uses the correctedposition data to search for and exclude device termination errors. Atthis point, all automated detection of device errors is completed, andany large deviations from the expected correlation between device events(e.g., photobeam time and lick number) and intake will be flagged forinspection. Such deviations may result from data entry errors or failureof the device to detect events. The experimenter will then use the GUIto view and process data flagged due to possible data entry or devicefailures, as well as data that has been flagged by the user forreview—for example, when the food hopper is very low, raising thepossibility that the animal had been food deprived.

FIG. 16 shows screen shots of Stage 1 QC GUI. Panels A and B show ascreen shot of the Stage 1 QC GUI showing the experimentround/mouse/date selection box (Panel A) and the Supplemental PlotChooser/Viewer (Panel B). (The screen shots shown here are made usingthe Matlab Guide GUI design interface, which allows one to placebuttons, plots etc in a design and to change their attributes (i.e.color, state, etc)). In the selection box, two error flags are showing,a “Cage boundary violation” and a “Drift difference violation”. Thedrift difference violation is clearly present in the position driftdifferences plot in Panel B. Because these stray licks do seem to beactual lick device failures, the user can now understand the origin ofthe second error (“Cage boundary violation”); since the mouse positionsduring lick events are used by the movement position correctionalgorithm to correct the movements, the bad lick positions flagged abovecause the mouse to appear to have moved beyond the cage, as shown inPanel C (a byproduct of the MPC tool). To correct both of these problemsthe user would use the GUI to exclude the bad licks by clicking the “3”radio button (in the “Le” column) and then rerunning the MPC tool. Asseen in Panel D, this procedure removes the cage boundary violation asexpected.

In certain embodiments, the excluded licks are automatically excluded bysimply excluding all licks whose drift differences were above somethreshold. However, as described above, the algorithm that calculatesthe drift difference relies on the accuracy of the algorithm thatestimates the movement drift. Estimating this drift is a non-trivialproblem, so in many embodiments there may be a need for the experimenterto check any flagged lick or feeding events using the Stage 1 QC GUI.Other flagged errors can also be examined in this manner.

FIG. 17 shows a screen shot of a Stage 2 QC GUI. Here we see an exampleof a flagged failure-to-detect error. Panel A in the figure shows nolicking events in the event plot except for one at the very end of theday. However, panel B shows that the amount of water that the mouseconsumed that day (value highlighted within square) is notanomalous—neither when compared with its intake on the other days of theexperiment, nor with the intake of other mice in the experiment. Thisindicates a lick device failure-to-detect error, and the lick event datafor that mouse and day must be excluded. However, the food intake andmovement data does not have to be excluded. Again, errors like this canbe checked by the experimenter using the Stage 2 QC GUI.

Further details of the user interface and displaying classificationresults are include in the attached Appendices 1 and 2.

3. Active and Inactive State Classification

Another aspect of the invention relates to the classification of activeand inactive states. In general, active states are states in which thereis an increased probability of some measured behaviors occurring(including movement), punctuated by inactive states during which theprobability of being in characteristic location(s) is high and restingand sleeping are likely to occur. Transitions between active andinactive states represent a basic feature of behavioral organization offreely acting animals. The methods and systems described below ofclassifying these states may be applied across species, etc. Also, asindicated, these methods may be used for classification of other states,beyond active and inactive states, in which there is a high probabilityof a behavior or behaviors occurring and/or a high probability of beingat characteristic locations.

In certain embodiments, approaches for automating the objectiveidentification of active and inactive states, which, as indicated, mayserve as fundamental features of behavioral organization, are provided.This allows detailed analysis of behavioral sequences and circadian andultradian influences on active state properties. Once active andinactive states are classified, temporal variations can becharacterized. Examples of this characterization are discussed furtherbelow, and in the Examples.

In certain embodiments, inactive state classification involves derivingan inactive position duration threshold. Positions with durations longerthan the inactive threshold are classified as inactive. To accuratelyand robustly identify this threshold, it was necessary to determine twoparameters: a time window and a spatial filter parameter. The timewindow is used to capture epochs in which a single home base is used;over some period of time animals may relocate their home base, forexample, a mouse may change the location of its nest, a person may gobetween two locations, spending some nights at one house and othernights at a second house, etc. Using a time window in which a singlehome base (whichever or wherever that base is) is used ensures thatsleeping and resting time spent at different nests, second homelocation, etc. are correctly identified as inactive states. A spatialfilter is applied to smooth out small movements that did not remove theanimal from the location of the home base, e.g., a person rolling overin bed, a mouse changing positions in the nest, etc. The optimalcombination of time window and spatial filter is selected by minimizinga state classification error.

FIG. 11 is a process flow sheet showing operations in a process ofclassifying active and inactive states. The process begins by selectinga time window and a movement threshold (1101). Raw data in a datasettypically includes movement (position vs. time) information over ameasurement period, e.g., 12 hours, 24 hours, 36 hours. As describedabove, movement data in a raw dataset is recorded at a threshold changein position. For example, for a threshold of 1 cm, movement informationis collected and stored in the raw dataset when the animal moves atleast 1 cm. Time windows may range from 0 to the measurement period,e.g., for a measurement period of 24 hours, from 0 to 24 hours, 1, 2, 4,6, 12, 24, etc. A spatial filter can be applied by choosing a movementthreshold, which may range from the data collection threshold, e.g., 1cm, 2 cm, 3 cm, etc.

An inactive state threshold is then selected to define inactive stateonsets and offsets (1103). As indicated above, the inactive statethreshold is a threshold duration of classifying a position as inactive.Determining the inactive state threshold is discussed further below withrespect to FIG. 12. It should be noted though that depending on themovement threshold under consideration, the inactive state thresholdresults in different inactive states. For example, if the inactive statethreshold is 1 hour, the classification of a state as inactive dependson the movement threshold: if an animal moves 10 cm in one hour, thestate is classified as inactive if the movement threshold is 15 cm, butnot if the movement threshold is 5 cm. Thus, once the inactive stateonsets and offsets are defined using the inactive state threshold forthe time window and movement threshold combination under consideration,an inactive state error percent is calculated (1105). This is discussedfurther below as well, but in certain embodiments, involves checking fordevice events occurring during states classified as inactive (duringwhich no such events should occur). States erroneously classified asinactive are then corrected, i.e., reclassified (1107). A total errorrate, i.e., one that includes erroneously classified active states maythen be calculated (1109). The entire classification and error rateprocess (operations 1101-1109) is then repeated for all combinations oftime window and movement threshold (1111). An inactive stateclassification (i.e., the classification of inactive states performed inoperation 1103 as corrected by operation 1107) is selected based on thetotal error rate (1113).

A. Determining an Inactive State Threshold Duration to Define InactiveState Onsets and Offsets

FIG. 12 is a process flow sheet illustrating operations in determiningan inactive state threshold. This is the minimum duration for aninactive state, i.e., the minimum duration during which the animal doesnot move (with a “move” being determined by the movement threshold asdescribed above). As described above (see operation 1103 of FIG. 11),the inactive state threshold duration defines the inactive state onsetsand offsets, thus providing higher order temporal classification of theanimal's behavior during the measurement period.

The process of determining an inactive state threshold begins by findingthe position during the time window/movement threshold underconsideration that has the longest duration or LDP (1201). The LDP willvary according to the time window and the movement threshold. Then, thedistances of all other positions from the LDP are obtained (1203). Thesedistances are plotted against the logs of the durations of thesepositions. An example of such at plot is shown at 1301 in FIG. 13. Ascan be seen from FIG. 13, this plot reveals a class or cluster of longpauses that are relatively close to the longest pause in that timewindow. The inactive state threshold duration is the duration at whichthe maximum distance from the LDP dramatically increases. In certainembodiments, this duration is found by binning the pause durations(1207) and determining the maximum distance from the longest pause foreach bin (1209). A least squares curve-fitting routine is then used tofit three lines to the maximum pause distance versus log pause duration(1211). See plot 1303 in FIG. 13. The intersection 1305 of the secondand third lines (i.e., where the maximum distance from the LDPdramatically increases) can be used to define the pause threshold forthe immobile state (1213). An inactive state can then be defined asconsecutive positions (or a single position) having a duration greaterthan the inactive state threshold (1215). From this criterion, theinactive state onset and offsets can be obtained (1217). Plot 1305 inFIG. 13 shows a group of inactive periods (red) in a cage revealed fromapplication of the inactive state pause threshold. These states arerestricted to the animal's nest location. Obtaining inactive stateonsets and offsets gives the active state onset and offsets, as well.

B. Calculating State Classification Error

As described above, finding the optimum time window/movement thresholdinvolves classification error rates. If the above method is accurate atclassifying inactive states, no device events should occur during theinactive states. In certain embodiments, determining intakeclassification error involves calculating the percent of inactive statesthat contain device events. Active state classification error can bedetermined, e.g., as the percent of active states that lack a deviceevent and during which the area covered by the animal is not greaterthan the maximum of all areas covered during inactive states. The stateclassification error can then be determined from both inactive andactive state classification errors, e.g., by summing the inactive andactive state classification errors. FIG. 14 is a process flow sheetshowing operations in one method of calculating state classificationerror. The process begins by receiving the inactive state onsets andoffsets (1401). Inactive states, i.e., the periods between the onsetsand offsets, that contain a device event are identified (1403). Theinactive state error rate is then calculated based on the number ofinactive states identified; in the example depicted in FIG. 14, theerror rate is the percentage of inactive states that contain a deviceevent (1405). This inactive state error is stored for use in calculatingthe total error rate, and thus the fitness of the movement threshold.The classification is then corrected such that no inactive statescontain a device event (1407). Correction of the inactive states isbased on the criteria used to define an inactive state, e.g.,consecutive positions having a duration greater than the inactive statethreshold; thus an inactive state having a device event may bereclassified into a single active state that is continuous withsurrounding active states, may be broken up into active and inactivestates, etc. After the corrections are implemented, the active stateerror rate is calculated, based on the updated classification (1409).According to certain embodiments, the active state error rate iscalculated by looking at active states in which there are no deviceevents (e.g., the animal does not eat, drink, interact with stimuli,etc.) and in which the animal does not cover a large area. In the flowsheet of FIG. 14, for example, the active state error rate is thepercentage of active states in which there are no device events andduring which the area covered by the animal is not greater than themaximum inactive state area. The areas of each active and inactive statemay be found by fitting convex hulls to the position data for eachinactive and active state. The total error rate may then be calculatedbased on the inactive and active state error rates.

4. Bout Classification

Behaviors within the active state are organized using the concept of about as a behavioral element. A bout is the repetition of a behaviorclustered together in time and without the intervention of a differentbehavior. Automated algorithms for bout identification, incorporatinginformation regarding both temporal and spatial properties of thebehavior are presented for the quantification of feeding, drinking andother behaviors. As described above, the data from which bouts areidentified includes device event information, e.g., photobeam breaksindicating the presence of a mouse at a feeder, etc. The processes ofthe invention allow, in an automated fashion, classification of thebehavior of into bouts of behavior and movement and in addition, ahigher level of organization-clusters of bouts.

For the identification of bouts, spatial information may be incorporatedinto the classification scheme by assessing the locations occupied bythe animals between the end of each device event and the onset of thesubsequent event at that device (inter-event intervals, IEIs). Incertain embodiments, if the animal left the device during an IEI, thenan intervening behavior had occurred. So, for example, the probabilitythat the animal remained at the device during an WI is estimated: if theprobability of remaining at the device is greater than 0.5, the IEI isclassified as being “at the device.” Temporal patterns of behavior arealso incorporated into the classification scheme: if events group intime to form bouts, then the IEI durations, IDs, should exhibit at leasttwo distinct types: IDs that are likely to occur within feeding boutsand IDs that are likely to occur between feeding bouts. (See, e.g.,Langton, S. D., Collett, D., and Sibly, R. M. (1995). Splitting BehaviorInto Bouts; A Maximum Likelihood Approach Behaviour 132, 781-799 andTolkamp, B. J., Allcroft, D. J., Austin, E. J., Nielsen, B. L., andKyriazakis, I. (1998). Satiety splits feeding behavior into bouts.Journal of theoretical biology 194, 235-250, both of which areincorporated herein by reference.) In certain embodiments, the IDdistributions are split into two groups (short and long) and theprobability that an IEI is short is estimated. The designation of eachIEI as either a within-bout interval (WBI) or an inter-bout interval(IBI) can then be made based on both the probability that the IEIoccurred at the device and the probability that it was short.

FIG. 15 is a process flow sheet showing high-level operations in amethod of organizing behavioral event information into bouts that usesboth spatial and temporal information. The process begins by receivingdevice event and movement information (1501). This information includesspatial information including the spatial position of the animal duringevents and inter-event intervals (IEIs). As indicated above, an IEI isthe interval between the onset of a device event and the onset of thesubsequent event at that device. The information received also includestemporal information including the duration of inter-event intervals.For the identification of bouts, spatial information is incorporatedinto the classification scheme by assessing the locations occupied bythe animal for each IEI. For each IEI, the position at which the animalwas furthest from the device (the maximally distant IEI position orMDIP) under consideration is determined (1503). The probability that theanimal remained at the device during an IEI is then estimated based onthe MDIPs for the device under consideration (1505). Temporalinformation is incorporated into the classification scheme by estimatingthe probability that the IEI is short (vs. long) based on theinter-event interval durations (IDs) for the device under consideration(1507). The IEI is then classified as being a with-in bout interval(WBI) or as an inter-bout interval (IB) based on the estimatedspatial-related and temporal-related probabilities, for example byaveraging the probabilities (1509). Unbroken strings of WBIs are thenclassified as being bouts (1511).

Evidence that this approach distinguishes populations of IEIs withdistinct spatial and temporal properties is depicted in FIG. 22C. Foreach IEI, the maximum distance from the feeder is indicated on theY-axis and the logarithm of its duration is indicated on the X-axis.IEIs designated as WBIs are shown in orange, and all occur in thevicinity of the feeder. During the vast majority of IBIs, animals strayfrom the feeder (green), with water intake occurring in a subset ofthese (blue). A small cluster of IEIs occur in the vicinity of thefeeder (red), but are classified as IBIs due to their long durations.Thus, using both spatial and temporal information for boutclassification produces different classification than using spatial ortemporal information alone.

A. Classifying the IEI as being at or Away from the Device

As described above, in certain embodiments, spatial information isincorporated into the bout classification scheme by estimating theprobability, or classifying, the WI as either being at or away from thedevice. In certain embodiments, this is accomplished by fitting amixture of bivariate normals to the MDIPs under consideration during theWI. The centroids of the fitted bivariate normals are clustered using arapid nearest neighbor clustering algorithm. The cluster of bivariatenormals whose centroid is nearest to the device is classified as “at thedevice” (AD). The bivariate normals in this cluster are assigned as ADbivariate normals (with the exception that diffuse bivariate normals maybe excluded.) The posterior probabilities for the AD bivariate normalsmay be summed to yield an estimate of the probability that eachmaximally distant JET position (MDIP) is at the device. In certainembodiments, the IEI is classified as occurring “at the device” if theprobability is 0.5 or higher. In certain embodiments, the probability isthen used, along with the temporal-related probability, to classify theIEI as being a within bout interval or an inter-bout interval, asdescribed above.

FIG. 22A shows an example of the results of fitting bivariate normals toMDIPs for a mouse in a cage. In the left hand panel, all positionsassigned to the nine bivariate normal distributions in the final fit aredisplayed with different colors and symbols. In the middle panel, onlythe centroids of the bivariate normal distributions are displayed withthe bivariate normal distributions classified as occurring at the devicedisplayed in orange and all other bivariate normal distributionsdisplayed in green. In the right hand panel, the MDIPs that wereclassified as occurring at the feeder are displayed in orange and allother positions are displayed in green indicating that locomotion awayfrom the feeder occurred during the IEI.

B. Classifying the IEI as Short or Long

To distinguish between IDs that are likely to occur within feeding boutsand IDs that are likely to occur between feeding bouts ID distributionsfor each animal are fit with mixtures of log normal distributions. Ithas been found that the ID distributions are best fit by a mixture of 3or more log normal distributions consistent with the presence ofdistinct types of IDs. The probability that an JET was short is thendetermined by splitting the log normal distributions into two groups(e.g., short and long) based on the probability that the animal remainedat the device.

In certain embodiments, the probability that an ID is short relative tothe overall distribution determined by fitting univariate normals to thelog transformed IDs. An example is shown in FIG. 22B, which shows fiveunivariate normals fitted to log transformed IDs. In FIG. 22B, the lognormal ID (min) is shown on the x-axis, with an unnormalized probability(the square root of the frequency of the ID) on the y-axis.

The duration data is then partitioned, by finding the posteriorprobability for each ID for each of the normal distributions. Topartition the data, the IDs are then sorted from shortest to longest.Each ID is hard clustered, i.e., the ID is indexed according to thenormal distribution it has the highest posterior probability ofbelonging to: in the example shown in FIG. 22B, each duration data pointwould have an index of 1, 2, 3, 4 or 5. The data is partitioned eachtime there is a change in the index, i.e., when the hard clusteredidentity changed from one cluster to another.

Spatial information is then used to classify partitions as having eithera short or long duration with short durations consistent with a highprobability of an IEI being a WBI (calculated as described above). Toclassify partition durations as short or long, all partitions for agiven group (e.g. OB mice) were combined to reduce the effects ofindividual variability. A smoothing line was then fit to the partitionAD (at device) probability as a function of the mean of the logtransformed partition durations. An example is shown in FIG. 33. For theWBI, a group duration criteria, ID_(WBI-group), was then set as theduration at which the animals were equally likely to remain at or leavethe device. All partitions with mean durations less than this criteriawhose partition AD probability is greater than 0.5 are classified asshort interval partitions. Similarly, all partitions with mean durationsgreater than the group duration criteria whose partition AD probabilityis less than 0.5 can be classified as long interval partitions.

For each animal, the transition between the short and long partitionscan be used as the short IEI duration criteria. Then the posteriorprobabilities for univariate normals whose mean duration is less thanthe duration criteria are summed to yield estimates of the probabilitythat each IEI was short.

5. Movement Bout Classification

Another aspect of the invention relates to methods for classifyingmovements during active states (AS) but not during other device eventbouts into locomotor movement (LM) or non-locomotor movement (NLM). Thisis done using a supervised learning algorithm that used the movementsoccurring during inactive states or during intake bouts as the trainingset. Because these movements take place in a limited area, theyrepresent “moving in place” (MIP) behavior and should reflect theproperties of NLM events. Thus, the MIP movements should be distinctfrom movements occurring during bouts of locomotion when the animalmoves around the cage or other measurement area. In one embodiment, toparameterize the training set of MIP events, the movement rate andturning angle (dot product angle of two movement vectors) for eachposition are used. Uninterrupted strings of movement events that weremost likely to occur during locomotion are then used to define the onsetand offset times of locomotion bouts. Finally, time within the activestates during which the animals are not engaged in behavioral boutsassociated with certain devices (e.g., intake) or locomotor bouts can beclassified as bouts of “other” behavior (e.g., scanning, rearing,grooming, digging, etc).

Further discussion of specific embodiments incorporating the movementbout classification are included below in the examples.

6. Comparison Clustering

Another aspect of the invention provides methods of using informationcollected from individual subjects to make comparisons among groups ofanimals to study influences of genes, drugs and environmental factors onthe neural regulation of behavior. Detailed quantitative assessment oftemporal patterns of behavior may provide a highly sensitive indicatorof the influence of such experimental manipulations on brain function.This requires analytical methods for detecting behavioral patterndifferences among experimental groups while accounting for thevariability in behavioral patterns occurring among individuals.

Novel methods for comparing behavioral patterns between two groups orpopulations (e.g., WT mice and OB mice) are provided. The comparisonclustering methods determines if patterns differ between two groups andidentifies aspects of the patterns that contribute most to any observeddifferences. An example of the algorithm is discussed with reference toactive state (AS) onset times and durations, though one of skill in theart will understand to apply it to other behavioral data.

The method involves testing the null hypothesis that two groups had thesame pattern, e.g., of AS onset times and durations. This isaccomplished by combining the AS onset times and durations for all daysin the two groups (which is appropriate under the null hypothesis) andassigning each AS in the combined data to one of a number of clusters.For each cluster, a chi-square statistic is then calculated based on thenull hypothesis that control and test group contributed an equalproportion of ASs to the cluster. The sum of the chi-squares over allclusters is used as the measure of difference in the daily pattern. Thesignificance of any difference can be determined by permuting theanimals between the two groups to obtain the percentile rank of theoriginal sum of chi-squares relative to the permuted sum of chi-squares.If there is a significant difference in the overall pattern, the partsof the pattern that contributed most to this difference are found byobtaining a p value for each cluster adjusted for multiple comparisonsusing stepwise resampling algorithm 3. See Troendle, J. F. (2000).Stepwise normal theory multiple test procedures controlling the falsediscovery rate. Journal of Statistical Planning and Inference 84,139-158, which is incorporated by reference herein.

FIG. 18 is a process flow diagram showing operations in a method ofcomparing two groups according to the certain embodiments. First, thedata from two comparison groups are combined under the null hypothesis(1801). Typically, a test group and a control group are the two groups,with the data the behavioral measurement or data under consideration forsubjects in each group. A number of clusters is selected (1803). Aprocess for choosing the optimal number of clusters is discussed furtherbelow. Then, each data point in the combined dataset is assigned to oneof clusters (1805). The chi-square statistic is calculated for eachcluster based on the null hypothesis (1807). The chi-squares are summedoverall all clusters (1809). As indicated above, this is a measure ofthe difference between the patterns of the two groups. The animalsubjects are then permuted between the two groups (1811). This is doneto test the difference between the two groups. If a significantdifference is present, the multiple comparison test is performed to findthe clusters that contribute to the difference in patterns (1813).

FIG. 19 is process flow diagram showing operations in a method ofchoosing the optimal number of clusters. As can be seen, it involvesminimizing the p value of the delta chi square between within andbetween group comparisons. The process shown in FIG. 19 is an example;one of skill in the art will understand variations and optimizations maybe made.

7. Computer Hardware

As should be apparent, certain embodiments of the invention employprocesses acting under control of instructions and/or data stored in ortransferred through one or more computer systems. Certain embodimentsalso relate to an apparatus for performing these operations. Thisapparatus may be specially designed and/or constructed for the requiredpurposes, or it may be a general-purpose computer selectively configuredby one or more computer programs and/or data structures stored in orotherwise made available to the computer. The processes presented hereinare not inherently related to any particular computer or otherapparatus. In particular, various general-purpose machines may be usedwith programs written in accordance with the teachings herein, or it maybe more convenient to construct a more specialized apparatus to performthe required method steps. A particular structure for a variety of thesemachines is shown and described below.

In addition, certain embodiments relate to computer readable media orcomputer program products that include program instructions and/or data(including data structures) for performing various computer-implementedoperations associated with at least the following tasks: (1) obtainingraw data from instrumentation, (2) performing automated anduser-interface data quality control, (3) classifying active and inactivestates, (4) analyzing and characterizing temporal variations in thesestates, (5) classifying behavioral bouts, (6) classifying movementbouts, (7) performing comparison clustering across groups. The inventionalso pertains to computational apparatus executing instructions toperform any or all of these tasks. It also pertains to computationalapparatus including computer readable media encoded with instructionsfor performing such tasks.

Examples of tangible computer-readable media suitable for use computerprogram products and computational apparatus of this invention include,but are not limited to, magnetic media such as hard disks, floppy disks,and magnetic tape; optical media such as CD-ROM disks; magneto-opticalmedia; semiconductor memory devices (e.g., flash memory), and hardwaredevices that are specially configured to store and perform programinstructions, such as read-only memory devices (ROM) and random accessmemory (RAM). The data and program instructions provided herein may alsobe embodied on a carrier wave or other transport medium (includingelectronic or optically conductive pathways).

Examples of program instructions include low-level code, such as thatproduced by a compiler, as well as higher-level code that may beexecuted by the computer using an interpreter. Further, the programinstructions may be machine code, source code and/or any other code thatdirectly or indirectly controls operation of a computing machine. Thecode may specify input, output, calculations, conditionals, branches,iterative loops, etc.

FIG. 20A illustrates, in simple block format, a typical computer systemthat, when appropriately configured or designed, can serve as acomputational apparatus according to certain embodiments. The computersystem 2000 includes any number of processors 2002 (also referred to ascentral processing units, or CPUs) that are coupled to storage devicesincluding primary storage 1906 (typically a random access memory, orRAM), primary storage 2004 (typically a read only memory, or ROM). CPU2002 may be of various types including microcontrollers andmicroprocessors such as programmable devices (e.g., CPLDs and FPGAs) andnon-programmable devices such as gate array ASICs or general-purposemicroprocessors. In the depicted embodiment, primary storage 2004 actsto transfer data and instructions uni-directionally to the CPU andprimary storage 2006 is used typically to transfer data and instructionsin a bi-directional manner. Both of these primary storage devices mayinclude any suitable computer-readable media such as those describedabove. A mass storage device 2008 is also coupled bi-directionally toprimary storage 2006 and provides additional data storage capacity andmay include any of the computer-readable media described above. Massstorage device 2008 may be used to store programs, data and the like andis typically a secondary storage medium such as a hard disk. Frequently,such programs, data and the like are temporarily copied to primarymemory 2006 for execution on CPU 2002. It will be appreciated that theinformation retained within the mass storage device 2008, may, inappropriate cases, be incorporated in standard fashion as part ofprimary storage 2004. A specific mass storage device such as a CD-ROM2014 may also pass data uni-directionally to the CPU or primary storage.

CPU 2002 is also coupled to an interface 2010 that connects to one ormore input/output devices such as such as video monitors, track balls,mice, keyboards, microphones, touch-sensitive displays, transducer cardreaders, magnetic or paper tape readers, tablets, styluses, voice orhandwriting recognition peripherals, USB ports, or other well-knowninput devices such as, of course, other computers. Finally, CPU 2002optionally may be coupled to an external device such as a database or acomputer or telecommunications network using an external connection asshown generally at 2012. With such a connection, it is contemplated thatthe CPU might receive information from the network, or might outputinformation to the network in the course of performing the method stepsdescribed herein.

In one embodiment, a system such as computer system 2000 is used as adata import, data correlation, and querying system capable of performingsome or all of the tasks described herein. Information and programs,including data files can be provided via a network connection 2012 fordownloading by a researcher. Alternatively, such information, programsand files can be provided to the researcher on a storage device.

In a specific embodiment, the computer system 2000 is directly coupledto a data acquisition system such as a microarray or high-throughputscreening system that captures data from samples. Data from such systemsare provided via interface 2012 for analysis by system 2000.Alternatively, the data processed by system 2000 are provided from adata storage source such as a database or other repository of relevantdata. Once in apparatus 2000, a memory device such as primary storage2006 or mass storage 2008 buffers or stores, at least temporarily,relevant data. The memory may also store various routines and/orprograms for importing, analyzing and presenting the data.

The invention may be embodied in a fixed media or transmissible programcomponent containing logic instructions and/or data that when loadedinto an appropriately configured computing device cause that device toperform one or more of the analytical operations described above on adataset (e.g. classify behavior into bouts, identify circadian patternsto behavioral bouts, classify within cluster behaviors, compare groups,etc.) according to the methods of this invention.

FIG. 20B shows digital device that may be understood as a logicalapparatus that can read instructions from media 2067 and/or network port2069. Apparatus 2050 can thereafter use those instructions to directanalysis of behavioral data, create, sort, search, and read behavioraldatabase, and the like. In certain embodiments, the digital device canbe directly connected to one or more cage behavioral systems accordingto this invention and, optionally function in realtime. In certainembodiments, the digital device can simply access, analyze, and/ormanipulate previously collected data.

One type of logical apparatus that may embody the invention is acomputer system as illustrated in 2050, containing CPU 2057, optionalinput devices 2059 and 2061, disk drives 2065 and optional monitor 2055.Fixed media 2067 can be used to program such a system and could canrepresent disk-type optical and/or magnetic media, and/or a memory orthe like. Communication port 2069 can also be used to program such asystem and can represent any type of communication connection (e.g. aconnection to a data acquisition system).

The invention also may be embodied within the circuitry of anapplication specific integrated circuit (ASIC) or a programmable logicdevice (PLD). In such a case, the invention may be embodied in acomputer understandable descriptor language that can be used to createan ASIC or PLD that operates as herein described.

The methods of this invention can be implemented in a localized ordistributed computing environment. In a distributed environment, themethods can be implemented on a single computer comprising multipleprocessors or on a multiplicity of computers. The computers can belinked, e.g. through a common bus, but more preferably the computer(s)are nodes on a network. The network can be a generalized or a dedicatedlocal or wide-area network and, in certain preferred embodiments, thecomputers may be components of an intra-net or an internet.

In certain internet embodiments, a client system typically executes aWeb browser and is coupled to a server computer executing a Web server.The Web browser is typically a program such as Microsoft's InternetExplorer, or NetScape or Opera. The Web server is typically, but notnecessarily, a program such as IBM's HTTP Daemon or other WWW daemon.The client computer can be bi-directionally coupled with the servercomputer over a line or via a wireless system. In turn, the servercomputer can be bi-directionally coupled with a website (server hostingthe website) providing access to software implementing the methods ofthis invention.

A user of a client connected to the Intranet or Internet can cause theclient to request resources that are part of the web site(s) hosting theapplication(s) providing an implementation of the methods of thisinvention. Server program(s) then process the request to return thespecified resources (assuming they are currently available). A standardnaming convention has been adopted, known as a Uniform Resource Locator(“URL”). This convention encompasses several types of location names,presently including subclasses such as Hypertext Transport Protocol(“http”), File Transport Protocol (“ftp”), gopher, and Wide AreaInformation Service (“WAIS”). When a resource is downloaded, it mayinclude the URLs of additional resources. Thus, the user of the clientcan easily learn of the existence of new resources that he or she hadnot specifically requested.

The software implementing the method(s) of this invention can runlocally on a server hosting the website in a true client-serverarchitecture. Thus, the client computer posts requests to the hostserver which runs the requested process(es) locally and then downloadsthe results back to the client. Alternatively, the methods of thisinvention can be implemented in a “multi-tier” format wherein acomponent of the method(s) are performed locally by the client. This canbe implemented by software downloaded from the server on request by theclient (e.g. a Java application) or it can be implemented by software“permanently” installed on the client.

In one embodiment the application(s) implementing the methods of thisinvention are divided into frames. In this paradigm, it is helpful toview an application not so much as a collection of features orfunctionality but, instead, as a collection of discrete frames or views.A typical application, for instance, generally includes a set of menuitems, each of with invokes a particular frame—that is, a form whichmanifest certain functionality of the application. With thisperspective, an application is viewed not as a monolithic body of codebut as a collection of applets, or bundles of functionality. In thismanner from within a browser, a user would select a Web page link whichwould, in turn, invoke a particular frame of the application (i.e.,subapplication). Thus, for example, one or more frames may providefunctionality for inputting and/or accessing ethograms for particularanimals or strains, while another frame provides tools for identifyingbouts, clusters, circadian patterns, and the like.

In addition to expressing an application as a collection of frames, anapplication can also be expressed as a location on the Intranet and/orInternet; a URL (Universal Resource Locator) address pointing theapplication. Each URL preferably includes two characteristics: contentdata for the URL (i.e., whatever data is stored on the server) togetherwith a data type or MIME (Multipurpose Internet Mail Extension) type.The data type allows a Web browser to determine how it should interpretdata received from a server (e.g., such as interpreting a .gif file as abitmap image). In effect, this serves as a description of what to dowith the data once it is received at the browser. If a stream of binarydata is received as type HTML, the browser renders it as an HTML page.If instead it is received type bitmap, on the other hand, the browserrenders it as a bitmap image, and so forth.

In Microsoft Windows, different techniques exist for allowing a hostapplication to register an interest in a data object (i.e., data of aparticular type). One technique is for the application to register withWindows an interest in a particular file extension for an (e.g.,.doc—“Microsoft Word Document”); this is the most common techniqueemployed by Window applications. Another approach, employed in MicrosoftObject Linking and Embedded (OLE), is the use of a class Globally UniqueIdentifier or GUID—a 16-byte identifier for indicating a particularserver application to invoke (for hosting the document having the GUID).The class ID is registered on a particular machine as being connected toa particular DLL (Dynamic Link Library) or application server.

In one embodiment of particular interest, a technique for associating ahost application with a document is through a use of MIME types. MIMEprovides a standardized technique for packaging a document object. Itincludes a MIME header for indicating which application is appropriatefor hosting the document, all contained in a format suitable fortransmission across the Internet.

In one preferred embodiment, the methods of the present invention areimplemented, in part, with the use of a MIME type specific to the use ofthe methods of this invention. The MIME type contains informationnecessary to create a document (e.g., Microsoft ActiveX Document)locally but, in addition, also includes information necessary to findand download the program code for rendering the view of the document, ifnecessary. If the program code is already present locally, it need onlybe downloaded for purpose of updating the local copy. This defines a newdocument type which includes information supporting downloadable programcode for rendering a view of the document.

The MIME type may be associated with a file extension of .APP. A filewith the .APP extension is an OLE Document, implemented by an OLEDocObject. Because the .APP file is a file, it can be placed on a serverand linked to using an HTML HREF. The .APP file preferably contains thefollowing pieces of data: (1) the CLSID of an ActiveX object, which isan OLE Document Viewer implemented as one or more forms appropriate tothe use of the methods of this invention; (2) the URL of the codebasewhere the object's code can be found, and (3) (optionally) a requestedversion number. Once the APP DocObject handler code is installed andregisters the APP MIME type, it can be used to download an .APP fileinto the user's Web browser.

On the server side, since the .APP file is really a file, the Web serversimply receives the request and returns the file to the client. When theAPP file is downloaded, the .APP DocObject handler asks the operatingsystem to download the codebase for the object specified in the .APPfile. This system functionality is available in Windows through theCoGetClassObjectFromURL function. After the ActiveX object's codebase isdownloaded, the .APP DocObject handler asks the browser to create a viewon itself, for instance, by calling the ActivateMe method on theExplorer document site. The Internet Explorer then calls the DocObjectback to instantiate a view, which it does by creating an instance of theActiveX view object from the code that was downloaded. Once created, theActiveX view object gets in-place activated in the Internet Explorer,which creates the appropriate form and all its child controls.

Once the form is created, it can establish connections back to anyremote server objects it needs to perform its functions. At this point,the user can interact with the form, which will appear embedded in theInternet Explorer frame. When the user changes to a different page, thebrowser assumes responsibility for eventually closing and destroying theform (and relinquishing any outstanding connections to the remoteservers).

In one preferred embodiment, from an end-user's desktop, the entry pointto the system is the corporate home or the home page of anotherparticular web-site. The page can, optionally, include, in aconventional manner, a number of links. In response to the user clickingon a particular link to an application page (e.g. a page providing thefunctionality of the methods of this invention), the web browserconnects to the application page (file) residing on the server.

In one embodiment, where the user requests access to the methods of thisinvention, the user is directed to a particular page type, e.g., anapplication (appdoc) page for in-place execution of an application(implementing one or more elements of the methods of this invention) inthe Web browser. Since each application page is located using an URL,other pages can have hyperlinks to it. Multiple application pages can begrouped together by making a catalog page that contains hyperlinks tothe application pages. When the user selects a hyperlink that points toan application page, the Web browser downloads the application code andexecutes the page inside the browser.

Upon the browser downloading the application page, the browser (based onthe defined MIME type) invokes a local handler, a handler for documentsof a type. ore particularly, the application page preferably includes aGlobally Unique Identifier (GUID) and a codebase URL for identifying aremote (downloadable) application to invoke for hosting the document.Given the document object and the GUID which arrive with the applicationpage, the local handler looks to the client machine to see if thehosting application already resides locally (e.g., by examining Windows95/NT registry). At this point the local handler can choose to invoke alocal copy (if any) or download the latest version of the hostapplication.

Different models of downloading code are commonly available. When codeis downloaded, a “code base” specification (file) is initially requestedfrom the server. The code base itself can range from a simple DLL fileto a Cabinet file (Microsoft .cab file) containing multiple compressedfiles. Still further, an information (e.g., Microsoft.inf) file can beemployed for instructing the client system how to install the downloadedapplication. These mechanisms afford great flexibility in choosing whichcomponent of an application gets downloaded and when.

In certain embodiments, the machinery employed for actually downloadingprogram code itself relies on standard Microsoft ActiveX API(Application Programming Interface)—calls. Although the ActiveX API doesnot provide native support for Web-delivered applications, its API canbe invoked for locating the correct version of the program code, copyingit to the local machine, verifying its integrity, and registering itwith the clients operating system. Once the code has been downloaded,the handler can proceed to invoke the now-present application host forrendering the document object (in a manner similar to invoking thehosting application through the registry if it were already installed).

Once the hosting application (OLE server) is loaded at the client, theclient system can employ the OLE document view architecture to renderthe application correctly within the browser, including usingconventional OLE methodology for adding the application's menu to thatof the browser and for correctly re-sizing the application upon are-size of the browser (as oppose to requiring the application toexecute within a single Active X control rectangle—the limitationpreviously noted). Once the application is executing at the client, itcan execute remote logic such as using RPC (Remote Procedure Call)methodology. In this manner logic which is preferably implemented asremote procedure(s) can still be used.

In certain preferred embodiments, the methods of this invention areimplemented as one or more frames providing the following functionality.Function(s) to organize, search, save, and retrieve raw behavioral dataor reduced/processed behavioral data (e.g. data produced by the devicesof this invention), functions to identify and/or classify bouts,functions to identify/classify clusters of bouts, functions toidentify/classify circadian patterns, functions to classify/identifywithin bout behaviors, functions to compare and contrast ethograms,functions to graphically represent ethograms, and the like.

In addition, the functions can also, optionally, provides access toprivate and/or public databases accessible through a local networkand/or the intranet whereby one or more ethograms contained in thedatabases can be input into the methods of this invention.

Methods of implementing Intranet and/or Intranet embodiments ofcomputational and/or data access processes are well known to those ofskill in the art and are documented in great detail (see, e.g., Cluer etal. (1992) A General Framework for the Optimization of Object-OrientedQueries, Proc SIGMOD International Conference on Management of Data, SanDiego, Calif., Jun. 25, 1992, SIGMOD Record, vol. 21, Issue 2, June,1992; Stonebraker, M., Editor; ACM Press, pp. 383 392; ISO-ANSI, WorkingDraft, “Information Technology-Database Language SQL”, Jim Melton,Editor, International Organization for Standardization and AmericanNational Standards Institute, July 1992; Microsoft Corporation, “ODBC2.0 Programmer's Reference and SDK Guide. The Microsoft Open DatabaseStandard for Microsoft Windows™ and Windows NT™, Microsoft Open DatabaseConnectivity™ Software Development Kit”, 1992, 1993, 1994 MicrosoftPress, pp. 3 30 and 41 56; ISO Working Draft, “Database LanguageSQL-Part 2: Foundation (SQL/Foundation)”, CD9075 2:199. chi. SQL, Sep.11, 1997, and the like).

Those skilled in the art will recognize many modifications can be madeto this configuration without departing from the scope of the presentinvention. For example, in a two-tier configuration, the server systemexecuting the functions of the WWW gateway may also execute thefunctions of the Web server. For example, any one of the above describedembodiments could be modified to accept requests from users/userterminals that are in a format other than a URL. Yet anothermodification would involve the adaptation to a multi-managerenvironment.

EXAMPLE

For the analysis of home cage behavioral patterns, mice wereindividually housed in home cage monitoring (HCM) cages for 14 days. Theinitial 4-days were considered an acclimation period, and the following10 days of data were used for the derivation and analysis of behavioralelements and their patterns. Obtaining multiple days of data for eachmouse allowed us to develop a detailed description of the average dailybehavior of each mouse and to assess the reproducibility of theunderlying behavioral elements and their patterns from day to day.

1. Experimental Procedures

A. Animals

Mice homozygous for the obese spontaneous mutation (Lep^(ob),B6.V-Lep^(ob)/J: OB) and control C57BL/6J mice (WT) were obtained fromThe Jackson Laboratory (Bar Harbor, Me.). Serotonin 2C receptorhemizygous mutant males (2C) bearing a null mutation of the X-linkedhtr2c gene (Tecott et al., 1995) and control WT litter mates were bredat UCSF by mating females heterozygous for the htr2c⁻ allele (congenicon a C57BL/6J background) with C57BL/6J males obtained from The JacksonLaboratory. Genotyping for the htr2c⁻ allele was performed by PCRanalysis. Animals were housed at room temperature (18-23° C.) on a 12-hrlight/dark cycle (lights on at 7 am) with free access to water and astandard chow diet (PicoLab Mouse Diet 20, Purina Mills, Richmond,Ind.). Experiments were performed in accordance with the guidelines ofthe National Institutes of Health Guide for Care and Use of LaboratoryAnimals and the University of California Committee on Animal Research.

B. Data Collection

Male mice were individually housed for 14 days in home cage behavioralmonitoring systems (HCM) consisting of 45×24×17 cm plexiglass enclosureswith feeders and water bottles mounted at one end. A wire ramp enabledentry into a 4×4 cm feeder, where animals could access powdered chow bydipping their heads through a 2.5×2.5 cm aperture into a food drawer. Todetect feeding, head dips interrupted a photobeam located below theopening in the ramp (DiLog Instruments, Tallahassee, Fla.). To detectdrinking, animals licked from a metal spout attached to a water bottlelocated behind a metal plate with a 0.5×2.5 cm aperture. The metal spoutallowed changes in capacitance from lick contacts to be detected (DiLogInstruments, Tallahassee, Fla.). To monitor the position of an animal'scenter of gravity, we placed the plexiglass enclosures on anactivity-monitoring platform with a central pivot point and two loadbeams at the front, (DiLog Instruments, Tallahassee, Fla.). Data wascollected to personal computers located in an adjacent room (DiLogInstruments, Tallahassee, Fla.). Intake event files recorded onsets andthe offsets of photobeam breaks and lick contacts, sampled everymillisecond. Movement events were defined as a change in an animal'scenter of gravity beyond a radius of 1 cm (calculated online from theanimal's body weight and the forces on two load beams after filteringwith a 500 millisecond moving average window). Movement event filesrecorded the onset of movement events sampled every 20 milliseconds aswell as the distance moved in x and y.

To determine food and water intake, data collection was stopped to weighfood and water after which data collection was re-started. This periodof daily maintenance occurred between 9 and 11 am and took less than 2hours. Each day, we recorded the animal's nest location in one of 21sectors defined by 3 divisions of the cage in x and 7 divisions in y. Wecollected data from 11-14 week old male mice: WT (n=8) and OB (n=8); WT(n=16) and 2C (n=16).

To validate the use of photobeam break time and lick contact number asmeasures of daily food and water intake, we weighed the food and waterfor a subset of mice at 7 am for 3 days, at 7 am and 7 pm for 3 days,and at 7 am, 1 pm, 7 pm, and 10 pm for 3 days after the initial datacollection. The strong correlation of intake with device measurementsacross different times of day confirm that these device measurements canbe used to estimate intake at any time of day (feeding R² (mean±sd,number of mice): WT 0.95±0.04 N=8, 2C 0.97±0.01 N=6; drinking R²: WT0.98±0.01, N=8, 2C 0.98±0.01 N=8; p<0.0001 for all mice both intakedevices). To estimate intake across time, we calculated a feedingcoefficient (mg/s) by dividing total chow intake by total photobeambreak time, and a licking coefficient (mg/lick) by dividing total waterintake by total lick contact number. By multiplying the feedingcoefficient by the duration of photobeam breaks or the lickingcoefficient by the number of lick contacts, we estimated intake acrosstime.

C. Data Processing and Quality Control

The large volumes of behavioral data required the establishment ofmethods for efficiently assessing and maximizing data quality. Toachieve this, we used the output of each data collection device tocross-check the performance of the other devices in the cage. The dataquality control algorithms developed in-house in the MATLAB programminglanguage automatically detected errors and flagged the data forexclusion or for review by an experimenter using a graphical userinterface built in-house. For ease of analysis, only mouse days witherror free data for all devices were used in the analysis.

i. Detection of Intake Event Onset Errors

We estimated the overall drift in positions (see movement positioncorrection below) and compared this with the drift in the positions ofthe animal during intake event onsets. To estimate the overall positiondrift, we calculated a position envelope (separately for x and y) thatfollowed how the minimum and maximum animal positions changed with timeduring daily data collection.

For example in x, this was accomplished by using the time seriesconsisting of positions in x and calculating multiple convex hulls whosevertices defined the envelope of the x positions with respect to time.Initially, we fit the first convex hull to x data in the first 15minutes of the day. Then this hull was expanded (in time) until themaximal distance that the animal traveled was at least 15 cm in x (or 35cm in y), indicating that the animal had traversed most of the cage (inx or y). Then we fitted the next convex hull encompassing the data inthe next 15 minutes after the end of the previous convex hull. This hullwas expanded in the same way as the first. We continued fitting hulls tothe x data until the whole daily data collection period was covered.

This produced a position envelope which defined the boundaries of theanimal's movement in the cage, providing estimates of how drift in theminimum and maximum x positions varied with time. The estimates of thedrift in maximum and minimum position were averaged to estimate thedrift in x position. If this drift differed from the apparent drift inintake device position by more than 10 cm in x or 15 cm in y, the intakeand movement device events were flagged for subsequent review. Eventonset errors led to the exclusion of photobeam break data for 3 mousedays and lick contact data for 4 mouse days.

ii. Movement Position Correction

Because we estimate positions using the forces on the activity platformload beams, errors could be introduced into these estimates by changesin the distribution of mass within the cage (due to removal of food andwater from the front of the cage, shifting of bedding, urination, anddefecation). To correct these errors, we first used the known locationsof the feeding and licking detection devices to set the expectedposition of the animal's center of gravity when at the device. Thisexpected position was set during the first device detection of the daysince this position will have a minimum amount of drift. Comparisonswere then made between subsequent intake event positions predicted frommovement data and the expected positions based on the location of theintake device (after excluding false intake event onsets as above). Ifthe predicted and expected positions differed by more than 2 cm in x ory (mean±sd: 33±9% of the movement events), we corrected positionsoccurring between the current and preceding intake event as follows: 1)the position drift was determined separately for x and y, 2) the totaldrift was apportioned among the positions weighted by the distance movedduring each movement event.

iii. Detection of Intake Event Offset Errors

Failure to detect the offset of intake events could occur when feederphotobeams became blocked by food particles. To detect such errors, allpositions across all days that a mouse assumed during photobeam breakswere clustered using a rapid nearest neighbor clustering algorithmtermed Ameoba. To identify distinct clusters occurring during photobeambreaks, we used a 5 cm cluster criterion. The presence of only onecluster, centered at the device, indicated that all the intake eventoffset times were accurate because the mouse was near the device duringall intake events. When more than one cluster was present, we assumedthat the largest cluster centered closest to the intake device containedvalid events. We excluded events in the other clusters since the animalwas far from the device during these events. This resulted in exclusionof photobeam break data from 68 mouse days out of 480 total mouse days(14%). The same algorithm was used to test for detection of lick eventoffset errors, but no such errors were detected.

vi. Detection of Other Errors

Data were also excluded for several idiosyncratic errors in datacollection. Photobeam break data were excluded for days 13-14 for onemouse that was observed sleeping in the feeder. All data were excludedfor days 12-14 for one mouse because it emptied the food hopper on day12 and may have been food deprived. All data were excluded for days 8-9for all mice in the WTOB cohort due to a loss of temperature control to31° C. in the monitoring room on day 8 for several hours.

D. Inactive State Classification

For each mouse, behavior was classified into two states: an inactivestate (IS) during which the mouse spent prolonged periods of time near asingle location, and an active state (AS) during which the animal movedaround the cage. This classification was accomplished by deriving aninactive position duration threshold as described above. Positions withdurations longer than the inactive threshold were classified asinactive. Because over some period of time animals may relocate theirhome base, we varied a time window to capture epochs during which asingle home base was used. A spatial filter was applied to smooth outsmall movements that did not remove the animal from the location of thehome base. To select the appropriate time window and spatial filter, weminimized a state classification error, as described above withreference to FIG. 11.

The time window was varied from 2-24 hours (2, 3, 4, 6 12, 24 hrsstarting at circadian time zero, (lights on)). As the spatial filter, weused a movement threshold that varied from 1 cm to 8 cm (1, 2, 3, 4, 5,6, 8 cm) which is close to the body length of these mice (ref). For eachcombination of time window length and movement threshold, we calculatedthe distance of all positions from the position having the longestduration in each window. Associated with each position was a durationand a distance to the longest duration position in that window. Thesepositions were then binned with respect to the log of their durations(bin width 0.1 log ms with empty bins excluded). Using non-linear leastsquares regression, we fit three lines to the maximum distance in eachbin. The intersection of the second and third lines was set as theinactive threshold for the mouse. We then defined IS onsets and offsetsby grouping adjacent inactive positions.

To determine the IS classification error, we calculated the percent ofISs that contained intake events. To determine the AS classificationerror, we identified ASs without intake events in which the area coveredby the mouse was not greater than the maximum of all areas coveredduring ISs. The state classification error was then calculated bysumming the IS and AS classification errors. We then selected themovement threshold that yielded the lowest state classification errorusing a 1×7 repeated measures ANOVA difference contrast (WTOB 2 cm; WT2C3 cm). The window duration did not significantly alter the error rateand was set to the largest window with the minimum number of inactivepositions greater than 10 centimeters from the longest pause (WTOB 12hrs; WT2C 4 hrs). Using these movement thresholds and time windows, thestate classification error rates for the cohorts were (mean±sd): WTOB7±10%; WT2C 5±10% and the inactive thresholds (mean±sd): WTOB WT 5±1 OB13±4; WT2C WT 8±2 2C 8±2, minutes. States classified in error werecorrected prior to further analysis.

E. Intake Bout Classification

For each mouse, we classified bouts separately for feeding and drinkingby examining the properties of all intervals between the offset of oneintake event and the onset of the next intake event (inter-eventintervals, IEIs). To classify each IEI into a within-bout interval (WBI)or an inter-bout interval (IBI), we examined two IEI properties: 1) theprobability that the mouse remained at the device during the IEI and 2)the duration of the IEI relative to the overall distribution of IEIdurations. After IEI classification, bout onsets and offsets wereidentified as unbroken strings of within-bout intervals between intakeevents. Classification of IEIs occurring during the light cycle wasperformed separately from classification of IEIs occurring during thedark cycle.

The probability that an animal was at the device during an IEI wasestimated by fitting bivariate normals (details of fitting procedurediscussed below) to the positions (x,y) that were the farthest away fromthe device during an IEI (FIG. 22A). The centroids of the fittedbivariate normals were clustered using a rapid nearest neighborclustering algorithm called amoeba. Ameoba allows a cluster to grow, inany direction, as long as any point in the cluster has a nearestneighbor closer than a user set distance criterion. The distancecriterion was varied from 1 to 2.4 cm yielding clusters of bivariatenormals. The cluster of bivariate normals whose centroid was nearest tothe device was classified as “at the device” (AD). The bivariate normalsin this cluster were assigned as AD bivariate normals with the exceptionthat diffuse bivariate normals (sd greater than 2 in x or y) wereexcluded. We then summed the posterior probabilities for the ADbivariate normals to yield an estimate of the probability that eachmaximally distant IEI position (MDIP) was at the device. The finaldistance criterion for amoeba was chosen to minimize the overlap betweenthe two groups by minimizing the classification entropy,

${E_{c} = {- {\sum\limits_{k = 1}^{N_{c}}{\sum\limits_{i = 1}^{N_{d}}{z_{ik}{\log\left( p_{ik} \right)}}}}}},{z_{ik} = \left\{ \begin{matrix}0 & {p_{ik} < 0.5} \\1 & {p_{ik} \geq 0.5}\end{matrix} \right.}$where p_(ik) is the at device posterior probability for position i andcluster k, N_(C) is the number of clusters and N_(d) is the number ofpositions (Biernacki et al., 2000; Celeux and Soromenho, 1996).

The probability that an IEI was short relative to the overalldistribution was determined by fitting univariate normals (details offitting procedure below) to the log transformed IEIs (FIG. 22B). We thensorted the IEIs from shortest to longest and defined partitionboundaries between consecutive IEIs where the hard clustered identity(z_(ik)) changed from one cluster to another. For individual mice, thisresulted in 3 to 9 partitions of the feeding IEIs and 4 to 15 partitionsof the drinking IEIs. The variation in the number of partitions resultedmainly from the variable number of peaks less than one minute forfeeding and less than one second for drinking. For feeding, thevariation across mice in the number of peaks may reflect differences inhow the mice handle the food (eg: head dipping vs paw feeding). Fordrinking, the variation across mice reflects differences in the numberof missed licks during bursts of licking with some mice frequentlymissing one or two lick contacts in a stream of highly stereotypedlicks. This produces one to three peaks that are narrower than expectedfor a normal distribution (kurtotic) such that each of these peaks mayrequire more than one normal distribution to provide an adequate fit tothe IEI distribution.

Because of the variation in the number of partitions, we utilizedspatial information to classify partitions as having either a short orlong duration with short durations consistent with a high probability ofan IEI being a WBI. Since we expected that intervals with shortdurations would be characterized by an increased probability ofremaining at the device, we examined the relationship between the meanduration in each partition and the probability that the animal was at anintake device (FIG. 33). The partition durations were then classified aslong or short in the following way. To classify partition durations asshort or long, we combined all partitions for a given group (e.g. OBmice) to reduce the effects of individual variability. We then fit asmoothing line (lowess, span 20% total number of data points) to thepartition AD probability as a function of the mean of the logtransformed partition durations (FIG. 33). For the WBI, a group durationcriteria, ID_(WBI-group), was then set as the duration at which the micewere equally likely to remain at or leave the device. All partitionswith mean durations less than this criteria whose partition ADprobability was greater than 0.5 were classified as short intervalpartitions. Similarly, all partitions with mean durations greater thanthe group duration criteria whose partition AD probability was less than0.5 were classified as long interval partitions. Some partitions (<1%)fit neither of these criteria and were given the classification of theirnearest neighbor partition.

For each mouse, the transition between the short and long partitions wasused as the short IEI duration criteria, ID_(WBIm). Then the posteriorprobabilities for univariate normals whose mean duration was less thanthe duration criteria were summed to yield estimates of the probabilitythat each IEI was short. For exceptionally diffuse univariate normals(sd greater than 1.5) the posteriors for IEI shorter than the criteriawere added to the short group and the posteriors for IEI greater thanthe criteria were added to the long group.

Finally, the probability that an IEI was a within-bout interval wasdetermined by averaging the probability that a mouse was at the deviceduring an IEI and that the IEI was short. We then classified an IEI aswithin-bout if this probability estimate was greater than a criteriagiven by

${{probability}\mspace{14mu}{criteria}} = \left\{ \begin{matrix}{{0.5 + {0.001*d_{IEI}^{\max}}},} & \begin{matrix}{{d_{IEI}^{\max} \leq {5\mspace{14mu}{cm}}},} \\{{ID}_{IEI} < {ID}_{WBIm}}\end{matrix} \\\begin{matrix}{0.505 + {0.005*}} \\{{d_{IEI}^{\max}/{\max\limits_{IEI}\left( d_{IEI}^{\max} \right)}},}\end{matrix} & {otherwise}\end{matrix} \right.$where d_(IEI) ^(max) is the maximum distance from the initial positionfor the IEI and ID_(IEI) is the duration of the IEI. This scaling of theprobability criteria places a greater weight on the at deviceprobability as the mouse moves farther from the initial position betweenintake events. The weighting was chose because the overlap of theposition bivariate normals was generally less than the overlap of theduration univariate normals. An upper limit for the amount of time thatcan be spent at the device between intake events was also set byclassifying as IBIs all IEIs whose duration was greater than the groupWBI duration criteria even if the animal had a high probability ofremaining at the device.

We further examined the properties of the intake bouts by fittingunivariate normal distributions to the log transformed bout sizes foreach mouse. These fits revealed that the bout size distribution wasbetter modeled by two or more log normal distributions. This was trueacross all mice. We therefore classified the bout sizes into large andsmall for each mouse by placing partition boundaries at the zeros of thefirst derivative of the univariate normal mixture fit. Bouts occurringin partitions that accounted for less than 15% of the total daily intakewere classified as small, and bouts occurring in partitions thataccounted for greater than or equal to 15% of the total daily intakewere classified as large. The small intake bouts contributed little tototal daily intake (feeding bouts: WTOB WT 4±2% OB 3±2%, WT2C WT 8±6% 2C10±5%; drinking bouts: WTOB WT 3±2% OB 1±1%, WT2C WT 5±4% 2C 8±9%,(mean±sd)), and were therefore not included in the analysis of intakebout properties (such as mean bout size and bout onset rate). However,the small bouts were included in the analysis of total time spentfeeding and drinking.

i. Univariate and Bivariate Normal Fitting:

Fitting of univariate and bivariate normal mixture distributions wascarried out using a regularized expectation maximization (rEM) algorithmwith regularization weight, lambda, set to 0.5 (Ormoneit and Tresp,1998; Ueda et al., 2000). To select the minimum number of normaldistributions that best fit the data, we started by fitting one normaldistribution to the data. We then tested the improvement in the fit tothe data resulting from the addition of each subsequent normaldistribution using the likelihood ratio (LR) between the two fits(LR=2*(log(L_(n+1))−log(L_(n)))) as the test statistic. The fitting ofadditional normal distributions continued until the estimated p value(calculated from a chi square distribution) for the comparison wasgreater than 0.01 for WBI and IBI classification and greater than 0.05for bout size splitting and comparison clustering. For the WBI and IBIclassification, the Wolfe correction for the estimate of the p valuecalculated from the chi square distribution was also used to decreasethe occurrence of overfitting (McLachlan, 2000).

The selection of the initial values used to initiate the rEM algorithmvaried with the number of normal distributions to be fit. For a singledistribution, fitting was initiated using the mean and variance of thedata as the initial parameter estimates. For a mixture of twodistributions, fitting was initiated using k-means clustering to provideinitial estimates of the mixture parameters. The k-means procedure wasinitialized from a uniform distribution covering the range of the data.For each rEM initialization, the k-means algorithm was run 1000-10,000times to increase the probability of finding the global minimum.

For a mixture of three or more distributions, fitting was initiatedusing a modification of the split and merge expectation maximizationalgorithm (Ueda et al., 2000). From the mixture distribution of theprior fit, each normal distribution was split into two normaldistributions. All combinations of splitting one normal distribution andretaining the remaining distributions from the prior fit were then usedto initialize fitting with rEM. The split that minimized the LR wasretained. Splitting of individual normal distributions was carried outby creating a local data set for each normal distribution and fittingeach local data set with two normal distributions using rEM initializedby k-means. Local data sets were created by estimating the local datadensity centered around each normal distribution in the mixturecalculated according to Ueda et al 2000 (equation 3.14). We then dividedthis density estimate by its maximum density to provide an approximatecumulative probability estimate for each data point. We then used thisdistribution to simulate the local data.

F. Movement Bout Classification

For each mouse, we classified movements occurring during the activestate but not during intake bouts, (M_(AS∉IB)), into locomotor movement(LM) or non-locomotor movement (NLM). We did this using a supervisedlearning algorithm that used the movements occurring during inactivestates or during intake bouts as the training set. Because thesemovements take place in a limited area, they represent “moving in place”(MIP) behavior (Drai et al., 2000) and should reflect the properties ofNLM events. Thus, the MIP movements should be distinct from movementsoccurring during bouts of locomotion when the animal moves around thecage. To parameterize the training set of MIP events we used themovement rate (cm/s) and turning angle (dot product angle of twomovement vectors) for each position (described below.)

For each mouse we defined the template for our supervised algorithmusing a kernel density estimator to assess the distributions of themovement rate and mean turning angle for MIP positions. The same kerneldensity estimator was used to assess these distributions for theM_(As∉IB) positions (FIGS. 34A-34D). The intersection of the twomovement rate probability densities, (MIP and M_(AS∉IB) represents thepoint where the M_(AS∉B) movement rate is equally likely to be similaror distinct from the MIP movement rate (cm/s (mean±sd): WTOB WT 1.1±0.2OB 0.23±0.03; WT2C WT 1.1±0.3 2C 1.5±0.3). The intersection of the twoturning angle probability densities, (MIP and M_(As∉IB))₅ represents thepoint where the M_(As∉IB) turning angle is equally likely to be similaror distinct from the MIP turning angle (deg (mean±sd): WTOB WT 47±5 OB65±6; WT2C WT 47±4 2C 45±3). The relative probability that the movementrate or turning angle of the M_(As∉IB) was distinct from the MIPpositions was estimated by dividing the probability density estimate forthe M_(As∉IB) by the sum of the probability density estimates for boththe MIP positions and the M_(As∉IB) positions. These relativedistributions (FIGS. 34A-34D) represent the probability that M_(As∉IB)movement rates or turning angles were distinct from the template ratesor angles. The relative probability estimates for movement rate andturning angle were averaged so that both movement rate and turning anglewere considered in the classification of each position. A position wasclassified as being within a LM bout if this averaged relativeprobability estimate was greater than 0.5. Finally, locomotion boutonsets and offsets were identified as uninterrupted sequences ofpositions with locomotion movements between them. If a locomotion boutcontained only a single position, the position was reclassified as MIP(<3% for all groups).

i. Determination of Position Movement Rate and Turning Angle

In general to estimate movement rate, at least two positions must besampled. We calculated movement rate for each position using a windowfive positions long because this is approximately half the body lengthfor these mice. To choose the best window, we compared 8 windows createdby shifting along a span of 9 positions, 4 on each side of the positionof interest. The window containing positions whose durations and turningangles were most similar to those of the position of interest wasselected as follows. For each of the comparison windows, we calculatedthe mean duration and mean turning angle by averaging the positiondurations and turning angles of each position in the window. The windowused to estimate the movement rate and turning angle of the position ofinterest was selected to minimize the distance between the duration andturning angle of this position and those assigned to the 8 windows. Themovement rate for this position was then calculated by dividing thedistance traveled from the first to the last position in the selectedwindow by the duration spent moving between these positions. Similarly,the turning angle for this position was calculated as the mean of theturning angles in the selected window.

Because data were collected using a 1 cm threshold, mice can stay at anindividual position for a prolonged period and move rapidly before andafter stopping at this position. Pauses of this type may be surroundedby rapid movements and misclassified as a locomotion positions using thesliding window described above. To detect such errors, we set a durationthreshold above which a single position was defined as a stop. To definethis threshold, we identified all intake bouts and inactive states thatcontained only a single position. The duration threshold was then setsuch that 95% of these positions were longer than the threshold. Inaddition, the threshold was not allowed to drop below 500 ms or to goabove 1000 ms for any individual mouse to prevent large variation inthis correction across groups (duration threshold, ms (mean±sd): WTOB WT593±113 OB 1000±0; WT2C WT 519±40 2C 664±156).

G. Comparison Clustering

Because the AS durations exhibit a complex pattern of variation withtime of day, a technique termed comparison clustering was developed totest if this daily pattern of AS onsets and durations differed betweentwo groups. In addition, if a difference was present, the comparisonclustering technique identified the parts of the daily pattern, definedby the AS onset times and durations, that contributed most to thedifference between the two groups.

Clustering of AS onset times and durations was carried out by fittingmixtures of bivariate normals to the combined data for two groups.Mixtures of bivariate normals were used to capture features of the dailypattern such as the grouping of AS onsets with long durations at thebeginning and end of the dark cycle. Because the AS durations range overseveral orders of magnitude, the durations were log transformed prior tobivariate normal fitting. In addition, the onset times and durationswere normalized to zero mean and unit standard deviation. After fittingof bivariate normals, distinct clusters were created by assigning eachAS to the bivariate normal with highest posterior probability (hardclustered). The chi-square statistic for each cluster was given by:

${\chi^{2} = {\frac{\left( {c_{o} - c_{e}} \right)^{2}}{c_{e}} + \frac{\left( {t_{o} - t_{e}} \right)^{2}}{t_{e}}}},$where c_(o)=observed number of control data points, t_(o)=observednumber of test data points, c_(e)=expected number of control datapoints, and t_(e)=expected number of test data points. The expectedvalues c_(e) and t_(e) were calculated by weighting the total number ofpoints in a cluster by the relative number of mouse days in the controland test data sets respectively (e.g.

$w_{c} = \frac{N_{c}}{N_{c} + N_{t}}$where N_(c)=control number of mouse days and N_(t)=test number of mousedays).

Increasing the number of clusters increases the resolution of thepatterns obtained, however it also decreases the sensitivity of the chisquare test statistic. To determine the number of clusters thatoptimizes the trade off between these two quantities, the control andtest groups were split in half multiple times by permuting the micewithin each group. The variation in the chi square statistic both withinand between groups was then examined in the following way. For eachpermuted data set, the sum of chi squares was calculated forwithin-group comparisons (control group 1 (cg1) vs control group 2(cg2), test group 1 (tg1) vs test group 2 (tg2)) and for between groupcomparisons (cg1 vs tg1, cg1 vs tg2, cg2 vs tg1, cg2 vs tg2) with thedata clustered into 1-50 clusters. The difference between the meanbetween-group and within-group chi-square statistics (delta chi-square)was used to calculate a p value based on the chi square distribution.The use of the delta chi-square in calculating the p values helps toaccount for the natural p-value variation within groups. The number ofclusters to use in comparing the full data sets was then chosen as thesmallest number of clusters with a delta chi-square p-value that was notstatistically significantly different by paired t-test from the minimumdelta chi-square p value.

H. Multiple Test Correction

Comparisons between groups were made for a number of different variablesusing t-tests or repeated measures ANOVA (Matlab). For a given level ofanalysis, a Bonferroni correction for multiple comparisons was used. Forinstance, in comparing the daily amount of food, water, and movement acorrection was made for three tests.

2. Results

A. Inactive State Classification

The spatial structure of mouse home cage behavior was examined byestimating the position probability density for individual animals (FIG.21A). Peaks in this distribution indicate positions where animals weremost likely to spend time each day. The probability density estimatestypically revealed a single prominent peak corresponding to the locationof the nest: the average distance from this peak to the observedlocation of the nest was 3±1 cm (mean±sd). Additional, smaller peaks atthe food hopper, water spout, and occasionally at other locations werealso observed. Thus, the data reveal a robust spatial structure of homecage behavior, with animals spending more time at the nest than at anyother location.

These findings suggested that the temporal structure of behavior in thehome cage may be organized around episodes of inactivity at the nest. Toinvestigate this possibility, we examined variation in the position ofindividual mice with time of day (FIG. 21B). Mice exhibited prolongedepisodes of time spent near a single location in the cage interspersedwith episodes of movement around the cage that were typicallyaccompanied by feeding and drinking. The location where mice spentprolonged episodes of time consisted of positions with long durationsbetween movements. To determine the extent to which these long positiondurations were spatially clustered, the relationship between positionduration and distance from the longest position duration (LPD) wasexamined (FIG. 21C). As position duration increased above severalminutes, there was a very rapid decline in the number of positionsfarther than about 5 cm from the LPD. An inactive position durationthreshold was identified as the position duration at which the distancefrom the LPD began to increase rapidly as described above with referenceto FIG. 12. Thus defined, the inactive position duration thresholdidentified positions which are clustered together in space and havelonger position durations than at any other location in the cage.

Using the inactive position duration threshold, inactive state (IS)onset and offset times were identified in an automated and reproduciblemanner. In addition, the location at which the IS positions clusteredwas identified and designated as the home base. As expected, thelocation of the home base typically corresponded to the location of thenest (FIG. 21D) with an average distance from the center of the homebase to the observed nest location of 2±1 cm (mean±sd). Finally, activesstates (AS) were classified as the temporal intervals between the ISsduring which animals engaged in locomotion, feeding, drinking, and otherbehaviors.

B. Bout classification

The organization of feeding and drinking within the active state wasinvestigated utilizing the concept of a bout as a behavioral element. About was defined as the repetition of a behavior clustered together intime and without the intervention of a different behavior. Automatedalgorithms for intake bout identification (such as those described abovewith reference to FIG. 15) incorporating information regarding bothtemporal and spatial properties of ingestion, were used forquantification of feeding and drinking behavior. The application of thealgorithm to classify feeding bouts in particular is described below.

For the identification of feeding bouts, spatial information wasincorporated into the classification scheme by assessing the locationsoccupied by mice between the end of each feeding event and the onset ofthe subsequent feeding event (inter-event intervals, IEIs). For eachIEI, the position at which the mouse was farthest from the feeder wasdetermined (FIG. 22A). These maximally distant IEI positions (MDIPs)appeared to cluster either in the vicinity of the feeder or at otherlocations in the cage (middle panel of FIG. 22A). This suggested acriterion for designating the termination of a feeding bout: if a mouseleft the feeder during an WI, then an intervening behavior had occurred.The probability that the mouse remained at the feeder during an IEI wastherefore estimated by fitting a mixture of bivariate normals to theMDIPs. If the probability of remaining at the feeder was greater than0.5, the IEI was classified as being “at the feeder” (right panel ofFIG. 22A, which shows orange IEIs classified as being at the feeder andgreen IEIs as being away from the feeder).

The next step in the identification of feeding bouts took into accountthe temporal patterns of ingestive behavior. If feeding events clusterin time to form bouts, then the IEI durations, ID, should exhibit atleast two distinct types: ID that are likely to occur within feedingbouts and ID that are likely to occur between feeding bouts. Todistinguish these distinct types, ID distributions for each mouse werefirst fit with mixtures of log normal distributions (FIG. 22B). For allmice, the ID distributions were best fit by a mixture of 3 or more lognormal distributions consistent with the presence of distinct types ofIDs. For log normal distributions with means less than about one minute,the probability that the animal remained at the feeder appeared to bevery high. This probability dropped rapidly for log normal distributionswith means greater than one minute (See FIGS. 22C and 33). Theprobability that an IEI was short was thus determined by splitting thelog normal distributions into two groups (short and long) based on theprobability that the mouse remained at the feeder.

Finally, the designation of each IEI as either a within-bout interval(WBI) or an inter-bout interval (IBI) was made by averaging theprobability that the IEI occurred at the feeder with the probabilitythat it was short. Evidence that this approach distinguishes populationsof IEIs with distinct spatial and temporal properties is depicted inFIG. 22C. For each WI, the maximum distance from the feeder is indicatedon the Y-axis and the logarithm of its duration is indicated on theX-axis. IEIs designated as WBIs are shown in orange, and all occur inthe vicinity of the feeder. During the vast majority of IBIs, animalsstray from the feeder (green), with water intake occurring in a subsetof these (blue). A small cluster of IEIs occur in the vicinity of thefeeder (red), but are classified as IBIs due to their long durations.

A further step in characterizing the behavior of the mice during ASs wasthe derivation of a method for distinguishing between locomotor movement(LM) and nonlocomotor movement (NLM) events. A supervised learningalgorithm was developed using “moving in place” (MIP) behavior (Drai etal., 2000) occurring during ISs or during bouts of feeding and drinkingas the NLM template. Uninterrupted strings of movement events that weremost likely to occur during locomotion were then used to define theonset and offset times of locomotion bouts. Finally, time within theactive states when mice where not engaged in intake or locomotor boutswhere classified as bouts of “other” behavior (e.g. scanning, rearing,grooming, digging, etc).

An example of bout classification during a single AS is shown for a WTmouse in FIG. 23A. Here, AS positions are plotted as in FIG. 21B, butwith an expanded time scale that permits the resolution of individualmovements between positions. In addition, bars above the feeding anddrinking event rasters at the bottom of the plot indicate the onset andoffset of feeding (orange) and drinking (blue) bouts. Positionsoccurring during locomotor bouts are indicated in green and revealedclear episodes of rapid movement between locations. By contrast bouts of“other” behavior (red) are frequently associated with NLMs in localareas such as at the feeder, lickometer and nest. This is highlighted inFIG. 23B, which displays the locations and durations of LM and NLMpositions, and in FIG. 23C, which displays the animal's locomotor pathsduring this active state.

C. Daily Amounts, Intensities, and Time Budgets

The classification of mouse behavior into ISs and into feeding,drinking, locomotor, and “other” bouts allows a detailed examination ofmouse behavioral organization in the home cage. At a general level,animals control their daily food and water intake as well as distancemoved by modulating the intensity of feeding, drinking, and locomotorbouts, as well as the amount of time spent in these bouts. (See FIGS. 24and 25). We anticipated that genetic perturbations of energy balanceregulation would impact daily amounts, times, and intensities of thesebehaviors. Relative to WT mice, OB mice exhibited a dramatic decrease indaily movement, accompanied by significant decreases in both theintensity of locomotor bouts and in the time spent engaged in thesebouts (FIG. 24). Although the chow intake of OB mice was significantlyelevated on the initial day of home cage monitoring (mean±se: WT 3.0±0.2g, OB 3.8±0.1 g, p=0.007), intake levels of WT mice subsequentlyincreased to levels that did not significantly differ from those of OBmice (FIG. 24A).

Perhaps the most striking perturbation of behavior in OB mice was analteration of their time budgets. These animals preserved the amounts oftime they spent feeding and drinking while significantly increasing theamount of time spent in the IS at the expense of time spent engaged inlocomotion and “other” behaviors (See FIGS. 24C and 25C, which presenttime budgets for WT, OB and 2C mice in the form of pie charts.) Thepreservation of time spent feeding and drinking, coupled with the markedincrease in IS time, led to substantial alterations in the proportionsof AS time spent feeding and drinking. During the active state, OB micespent 41±2% of their time feeding and 4.3±0.2% of their time drinkingcompared with WT mice, which spent 19±1% and 1.9±0.1% of their timefeeding and drinking (mean±se, p<0.0001 for feeding and drinking).

Relative to WT mice, 2C mice exhibited significant increases in dailyintake and movement, accompanied by significant increases in feeding andlocomotion bout intensities (FIG. 25A) without significant changes inthe amount of time spent in feeding and locomotion bouts (FIG. 25B).Unlike the OB mice, 2C mice significantly decreased the amount of timespent in the IS and significantly increased the amount of time spentengaged in “other” behavior. In addition, the 2C mice exhibited a trendtoward decreased time spent feeding, and as a result, the 2C mice spentonly 12±1% of the AS feeding compared with 19±1% of the AS spend feedingby WT mice (mean±se, p=0.00008).

D. Daily State Patterns

To determine how behavioral organization varies with time of day, weexamined the variation in IS and AS properties with circadian time. InFIG. 26A, representative patterns of ASs and ISs for single mice of eachgenotype for a 24 hr period are displayed. To illustrate thereproducibility of these daily patterns for individual mice, rastersdisplaying movement, feeding, and drinking events are displayed for 8days with AS classifications shown above each day (FIG. 26B). Toillustrate the reproducibility of these daily patterns across mice, thedurations of ASs (FIG. 26C) and ISs (FIG. 26D) versus time of day arealso displayed for these individual mice, and superimposed on data fromthe other animals in their cohorts.

Examination of these records reveals that AS durations in WT miceexhibited marked circadian variation with the longest durationsoccurring at dark cycle (DC) onset and offset. The IS durations alsovary markedly with time of day with the longest durations occurring inthe middle of the light cycle (LC) and DC. Qualitative comparisonsuggests that OB mice had greatly reduced numbers of short ASs andexhibited less circadian variation in AS durations. Strikingly, the longASs at DC onset and offset appear to be absent in OB mice. In contrast,circadian variation in IS duration in OB mice seems relatively similarto that of WT mice but with an overall increase in IS duration. Unlikethe OB mice, the overall pattern of AS durations in the 2C mice appearrelatively similar to the WT pattern except for an apparent increase inthe number of LC ASs. The overall pattern of IS durations in the 2C micealso appears relatively similar to the WT pattern except for an increasein the number of LC ISs.

To quantify these apparent differences in the circadian organization ofAS and IS patterns, phenotypic comparisons were made using repeatedmeasures ANOVA for AS probability, onset rate, and duration as well asIS duration. For all the state properties, there were highly significanteffects of circadian time (FIGS. 27 and 28). The AS probabilities forall groups exhibited clear peaks at DC onset and offset, indicative of acrepuscular pattern (most active at dusk and dawn), rather than a simplenocturnal pattern (FIGS. 27A and 28A).

The WTOB comparison revealed the OB mice to exhibit decreased ASprobabilities (FIG. 27A), decreased AS onsets (FIG. 27B), and increasedIS durations (FIG. 27D). A significant effect of genotype on ASdurations was not detected (FIG. 27C). However, the interaction ofgenotype with time was significant for all the state properties. Thus,the marked increase in AS probability at DC onset and offset exhibitedby WT mice was greatly diminished in OB mice. In contrast, the nadirs inAS probability during both the DC and LC were similar, at which times,OB mice exhibit increased AS durations with low AS onset rates relativeto WT mice.

Because the AS durations exhibited a complex pattern of circadianvariation, we developed a novel algorithm for comparing these patternsbetween groups which we call comparison clustering. Comparisonclustering determines if patterns of variation in state duration withcircadian time differ between two groups and identifies aspects of thepatterns that contribute most to any observed differences (details givenabove). Comparison clustering analysis identified several featuresaccounting for distinct phenotypic differences in the circadian patternsof AS durations in WT and OB mice (FIG. 27E). For example, the long ASdurations initiated around DC onset and offset in WT mice weredemonstrated to be absent in OB mice. In addition, throughout the dayshort duration ASs were found to be markedly decreased in OB mice.

The WT2C comparison also revealed a marked phenotypic effect oncircadian patterns of ASs and ISs. FIG. 28 reveals significant effectsof the mutation: AS probability (increased; FIG. 28A)), AS onset rate(increased; FIG. 28B) and IS duration (reduced; FIG. 28D). A significanteffect of genotype on AS durations (FIG. 28C) was not detected. However,interactions between genotype and time were significant for all stateproperties. For AS probability, 2C mice increase the probability ofbeing in the AS in anticipation of the DC to a greater extent than WTmice, and continue to exhibit increased AS probability across the DC(FIG. 28A). The increase in AS probability during the LC is accompaniedby a marked increase in the rate of AS onsets (FIG. 28B) along with adecrease in IS duration (FIG. 28D). In contrast, the increase in ASprobability during the DC is primarily accompanied by a decrease in ISduration. Comparison clustering reveals that the increase in LC ASonsets is predominantly attributable to an increase in ASs of about 1-5minutes in duration in the six hours preceding the DC (FIG. 28E).

In addition to examining the impact of the energy balance mutations onAS durations, we also determined their impact on the amount of food andwater consumed and movement occurring during the ASs. This revealedmarked differences in the effect of the lep and 5htcr gene mutations onthe composition of the ASs. While the WTOB comparison did not reveal asignificant effect of genotype on AS durations, there were significanteffects of genotype on AS food (mean±se: WT 148±14 OB 361±20 mg) andwater (WT 126±8 OB 310±15 mg) intake with both being dramaticallyincreased as well as a on AS movement (WT 18±2 OB 6.7±0.6 m) which wasmarkedly decreased (FIGS. 36A-36D). In contrast, the WT2C comparison didnot reveal a significant effect of genotype on AS durations, AS food andwater intake, nor on AS movement (FIGS. 37A-37D).

E. Daily Bout Patterns

Phenotypic influences on circadian patterns of intake and locomotor boutproperties were examined using repeated measures ANOVA. Across allgroups, the daily patterns of intake and movement as well as the numberof bouts per hour exhibited a crepuscular pattern (FIGS. 29A1-29A2,29B1-29B2, 30A1, 30A2, and 30B1-30B2) similar to that of AS probability(FIGS. 27A, 28A). In contrast, such a pattern is not observed for boutsize (FIGS. 29A4, 30A4), or when numbers of bouts are expressed as afunction of time spent in the AS (FIGS. 29A3, 30A3). This suggested thatcircadian influences on intake and movement largely result fromcircadian variations in the probability of being in AS, rather thancircadian effects on bout size or AS bout rate.

To test this possibility, we used multiple linear regression withdominance analysis (Azen and Budescu, 2003; Budescu, 1993) to determinethe extent to which circadian patterns of intake and movement wereattributable to the predictor variables: AS probability, AS bout rate,and bout size. For feeding and drinking, the predictor variablesaccounted for 72-92% of the circadian variation in intake. Notably, ASprobability accounted for the majority of the circadian variation(65-88%) while AS bout rate and bout size accounted for a smallerproportion of the variance (0.3-18%). The predictor variables alsoaccounted for a large proportion of the circadian variation in movement(92-96%). The AS probability accounted for most of the circadianvariation in movement (49-60%) for both WT groups and the 2C mice.However, compared with intake, the AS bout rate accounted for a largerproportion of variance in movement (33-40%). In contrast, for OB mice,the AS probability accounted for 90% of the circadian variation inmovement while the AS bout rate accounted for only 5% of the variance.Altogether, these findings suggest that circadian influences on AS onsetrate and duration play a larger role in shaping circadian variation inintake and movement than do circadian influences on bout properties.

Comparison of the daily pattern of chow intake for WT and OB micerevealed a significant interaction of genotype and time (FIG. 29A1).However, the circadian influences on amounts of food consumed appearrelatively small compared to the large phenotypic differences observedin feeding bout properties. OB mice displayed a large and significantdecrease in bout rate that was most striking during the DC (FIG. 29A2).The OB AS bout rate was also significantly decreased throughout the day(FIG. 29A3) accompanied by a marked increase in bout size (FIG. 29A4).Drinking bout properties also exhibited significant effects of genotypewith the OB mice exhibiting a decrease in drinking bout rate accompaniedby a compensatory increase in drinking bout size (FIGS. 38A2 and 38A4).Similarly, FIGS. 39A1-39B4 show drinking and “other” bout properties forWT and 2C mice in each panel.

The large and significant decrease in daily movement exhibited by the OBmice also demonstrated a significant interaction of genotype with timeof day; differences were largest during the DC (FIG. 29B1). This wasreflected in a similar pattern in the bout rate (FIG. 29B2). Inaddition, the AS locomotion bout rate was substantially decreased,particularly during the DC (FIG. 29B3). By contrast, the averagelocomotor bout distance was only slightly decreased in OB mice (FIG.9B4). These results indicate that the hypolocomotor phenotype of OB miceresults both from a decrease in AS probability and in AS bout rate.

Comparison of the WT and 2C daily chow intake patterns revealed asignificant effect of genotype as well as an interaction of genotype andtime. The increased intake exhibited by 2C mice occurred predominantlyin the 8 hours preceeding the DC (FIG. 30A1). Interestingly, 2C miceexhibited an increase in feeding bout rate during this time (FIG. 30A2)but did not exhibit increased bout sizes (FIG. 30A4) or increased ASbout rates (FIG. 30A3). This suggests that the increased AS probabilityin 2C mice contributes substantially to the increase in chow intakepreceding the DC. Consistent with this, comparison clustering revealedthat 2C mice exhibited an increase in the number of ASs of 5-10 minuteduration over approximately 6 hours prior to DC onset (FIG. 28E).Notably, a large proportion of the ASs in this region contain feedingbouts without drinking bouts (WT 71% 2C 81%). This proportion wasmarkedly enhanced compared to the proportion of all ASs containingfeeding without drinking bouts (WT 29% 2C 39%). A selective increase in2C mice of ASs with a high priority for feeding thus appears to occurpreceeding the DC when these mice exhibit increased food intake.

A significant increase in daily movement was also observed in 2C mutants(FIG. 30B1), accompanied by a significant increase in the rate of LMbouts (FIG. 30B2). By contrast, there was not a significant effect ofgenotype on AS LM bout rate (FIG. 30B3) or on LM bout distance (FIG.30B4). These results indicate that the hyperlocomotor phenotype of 2Cmice results predominantly from an increase in AS probability.

While neither LM nor feeding bout sizes were altered in 2C mice, we didfind that the durations of both LM (mean±se: WT1.62±0.07 2C 1.30±0.03sec) and feeding (mean±se: WT 58±4 2C 46±4 sec) bouts were decreasedcompared with WT mice (RM ANOVA: LM G 0.0007 T 1.5×10⁻²¹ G×T 0.3; Chow G0.004 T 2.8×10⁻⁵⁵ G×T 0.2). It seems likely that the conservation of LMand feeding bout sizes results from the increased LM and feeding boutintensities observed in 2C mice (FIG. 25B).

F. Within Active State Structure

To examine the temporal organization of behavior within the activestate, we aligned all LC AS onsets for each mouse and then determinedthe probabilities of feeding and drinking, LM and “other” behaviors.Peri-event histograms displaying feeding, drinking, and movement eventswithin the aligned ASs revealed a striking regularity in structure(FIGS. 11 and 12). Mice were most likely to feed early in the ASs atwhich time there was a decrease in the probability of drinking,locomotion, and “other” behavior. Later in the ASs, mice were mostlikely to engage in bouts of “other” behavior. In accord with thisobservation, the effect of within-AS time on the probability of allbehaviors was highly significant for both WTOB and WT2C comparisons. Inaddition, OB mice maintained a high probability of feeding at thebeginning of the AS for much longer than the WT mice (FIG. 31B1). Thiswas likely the result of the much longer feeding bouts exhibited by theOB mice. Moreover, OB mice exhibited delayed increases in probabilitiesof drinking, locomotion, and “other” behaviors within the AS (FIGS.31A-31F). The WT2C comparison also revealed phenotypic influences onwithin-AS patterns, with the mutants exhibiting a more rapid decline inthe probability of feeding accompanied by an early increase in theprobability of engaging in “other” behaviors (FIGS. 31A-31F).Examination of within-AS patterns during the DC revealed a similarpattern of transitions in the probability of feeding, drinking,locomotion, and “other” bouts across all groups as well as similardifferences between groups (data not shown).

3. Discussion

In a freely acting animal, behavioral organization results from thefunction and interaction of multiple physiological and behavioralsystems. To quantitatively examine this organization in the mouse, wehave developed an automated and reproducible method for describing thespatial and temporal structure of mouse home cage behavior. Thisdescription includes the identification of basic units of behavior(bouts of feeding, drinking, and locomotion) and a characterization ofthe temporal organization of these bouts into ASs. The ability toquantitatively describe home cage behavioral patterns provides anopportunity to uncover fundamental characteristics of behavioralorganization and a powerful approach for assessing the roles of neuralcircuits in behavioral regulation. The utility of this approach ishighlighted by its application to two mouse lines bearing geneticmutations that alter energy balance. Profound phenotypic influences onstate and bout properties provide new insights into the manner in whichthese mutations impact behavioral regulation.

A. State Classification

An initial step in quantifying the organization of home cage behaviormade use of the key observation that the behavior of mice alternatedbetween two discrete states: active and inactive. This was revealed byqualitative examination of home cage behavioral records which showedmovement around the cage clustering with feeding and drinking. Theonsets and offsets of clusters of these behaviors were defined byprolonged episodes in which animals exhibited minimal movement in thevicinity of their nests. This is reminiscent of behavioral patternsobserved in natural environments (Adams and Davis, 1967; Brown, 1966;Gray et al., 1998; Halle and Stenseth, 2000b; Herbers, 1981). Manyanimals exhibit a characteristic use of space in which they regularlyforage and patrol within their home range and return to a refuge forrest or sleep. Animals thus appear to make transitions between ASs andISs which are distinct not only in terms of the location of the animaland the behaviors in which animals engage but also in terms of markeddifferences in energetic costs and risks of predation (Lima and Dill,1990). Transitions between ASs and ISs thus reveal prominent changes inthe state of the animal and represent a basic feature of the behavioralorganization of freely acting animals.

To quantify the occurrence of ASs and ISs, we utilized our ability tocharacterize the spatial structure of home cage behavior to identify ISsas episodes in which mice spent more time near their nest than at anyother location in the cage. In turn, this allowed us to quantify theclustering of feeding, drinking, and locomotion into ASs. Individualmice exhibited AS onsets and durations with a complex yet stable patternof circadian variation. In addition, single gene mutations disruptingenergy balance produced consistent and dramatic alterations in ASpatterns allowing us to assess the extent to which state propertiescontributed to phenotypic differences. The development and automation ofa principled method for detecting ASs and ISs in the laboratory mousethus enables the use of a broad array of experimental manipulations(genetic, environmental, pharmacological, lesions, etc) for examiningthe regulation of this fundamental unit of behavioral organization.

B. Bout Classification

Within ASs, we observe that occurrences of particular behavioral events,such as feeding, appeared to cluster together in time. Such clusteringof behavioral events has long been recognized in both natural andlaboratory settings and defines a basic unit of behavior called a bout(Berdoy, 1993; Collier and Johnson, 1997; Machlis, 1977; Mayes andDuncan, 1986; Morgan et al., 2000; Mori et al., 2001; Shull et al.,2001; Waggoner et al., 1998). The ability to quantitatively identifybouts enables the examination of this basic unit of behavior and theorganization of behavioral events within the AS.

Commonly, the identification of a bout has involved the division of IEIdurations into 2 types: short within bout intervals (WBIs) and longerinterbout intervals (IBIs) (Langton et al., 1995; Tolkamp et al., 1998).However, when more than one behavior is being observed, then a bout ofone behavior may also be defined to end when a different behavior begins(Machlis, 1977). If that intervening behavior is brief, then a boutcriteria based on WI duration alone may fail to properly detecttermination of a bout. Here, we developed a novel approach utilizingboth the WI duration distribution and the location of the animal toimprove bout classification. This approach allowed us to correctlyidentify short IBIs during which the animal left an intake device. Thesewould have been misclassified as WBIs using a duration criteria alone(28% of IBIs).

In addition, the use of spatial information was essential to developinga robust automated algorithm for the accurate identification ofingestive bouts. We used spatial information to capture common featuresof the IEIs overcoming the common problem of variability in durationdistributions of individual animals (Berdoy, 1993; Davison, 2004;Tolkamp and Kyriazakis, 1999). Spatial information allowed us to clearlydivide the IEI duration distributions into two groups (short IEIs andlong IEIs) without making assumptions regarding the number of log normaldistributions required to fit the data for each mouse (FIG. 33).

During the ASs, mice moved around the cage between bouts of feeding anddrinking with a characteristic pattern in which rapid movement betweenlocations alternated with long pauses and small movements in localareas. A similar pattern of movement has also been observed in rodentsexploring novel environments (Eilam and Golani, 1989; Golani et al.,1993), and quantification of this movement pattern has provided insightsinto the organization of exploratory behavior (Drai et al., 2000; Draiand Golani, 2001; Tchernichovski and Benjamini, 1998; Tchernichovski etal., 1998; Tchernichovski and Golani, 1995). In fact, this type ofintermittent locomotion occurs in a wide variety animals and behavioralcontexts such as foraging and patrolling in natural environments (Kramerand McLaughlin, 2001). It is thought that pauses may increase enduranceand the capacity of the animal to detect relevant stimuli. This patternthus appears to reflect a general feature of the organization ofmovement in multiple contexts. To quantify this pattern of movement, wetook advantage of the character of movements occurring during ingestivebouts and ISs to develop a supervised learning algorithm. Thisclassified movement during the ASs into bouts of locomotion andnon-locomotor movement. One intriguing observation resulting from thisclassification was that while locomotor bouts only account for 4% ofdaily time they account for 76% of the total distance moved each day.

C. Levels of Behavioral Organization

Having devised procedures for defining ASs and the bouts of behavioralevents that occur within them, we considered relationships between theselevels of behavioral organization. Prior studies on the temporalstructure of ingestive events described not only the existence of bouts,but also a higher level of organization in which bouts are clusteredtogether in time (Berdoy, 1993; Machlis, 1977; Tolkamp and Kyriazakis,1999; Yeates et al., 2001; Zorrilla et al., 2005). Whereas such studiesprimarily focused on the organization of particular behaviors inisolation, we observed that bouts of feeding, drinking and locomotionare all clustered together within ASs. This suggests that the mechanismsresponsible for this clustering are not unique to particular behaviors.The classification of ASs and ISs thus appears to capture a fundamentaltransition in the state of the animal characterized by the higher ordercoordinated organization of bouts into clusters of multiple behaviors.

Subsequent analyses revealed a characteristic pattern of temporalinterrelationships among the diverse behaviors that occur within the AS.Using the AS onsets as time zero, we calculated the time variation inthe probability of engaging in particular behaviors during ASs. Thisrevealed a clear sequential structure. The probability of feeding washigh early in the AS, associated with a decreased probability ofdrinking, locomotion, and “other” behaviors. As the feeding probabilitydeclined, the probability of engaging in bouts of “other” behaviorincreased and eventually the AS ended. This suggests that there areorderly transitions in an animal's behavioral priorities during an AS.Interestingly, this temporal structure is reminiscent of the behavioralsatiety sequence (BSS): a sequence of behaviors observed in animals withaccess to highly palatable foods or after food deprivation (Antin etal., 1975; Ishii et al., 2003). In these instances, animals initiallyengage in feeding followed by grooming, sniffing, rearing, locomotionand then rest. The similarity of within AS structure to the BSS suggeststhat the transitions between behavioral priorities occurring in bothcases are similar. This is also suggests that the goal of obtaining foodmay be a primary determinant of AS initiation in the home cage.

The ability to characterize the properties of states and bouts enablesus to determine the level(s) of organization through which biologicalprocesses and experimental manipulations shape behavioral patterns. Anillustrative example relates to the manner in which circadian influencesproduce the characteristic crepuscular pattern of ingestive behavior inC57BL/6 mice. One means by which animals could vary intake with time ofday would be to vary behavior at the level of bout properties such asduration, intensity, and resultant bout size. Another means would be tovary the rate of bout onsets within the AS (AS bout rate) or at a higherlevel of organization by varying the transition rates and durations ofthe ASs and ISs. Examination of behavior at these levels of organizationrevealed that while AS intake bout rate and bout size variedsignificantly with time of day, variation in AS probability explainedmost of the circadian variation in intake. These results suggest thatthe crepuscular pattern of intake in C57BL/6 mice results primarily fromcircadian influences at the level of state transitions and durations,rather than from changes in intrinsic properties of ASs or from changesin bout properties.

D. Energy Balance Mutants

The utility of a system enabling the continuous quantitative assessmentof diverse behaviors across multiple levels of organization ishighlighted by new phenotypic insights into OB mice, a line that hasbeen extensively investigated for nearly 60 years. One striking findingwas the large increase in IS time in OB mice and the manner in whichthis was achieved. This increase was achieved at the expense of timespent in bouts of locomotion and “other” behavior. By contrast theamounts of time spent feeding and drinking were preserved. As a result,a doubling of the percent of time devoted to feeding and drinking duringthe AS was observed in OB mice. This shift in the use of time reveals adramatic rearrangement in the behavioral priorities of OB mice with anincreased priority placed on remaining in the IS at the expense of timespent engaged in locomotion and “other” behaviors.

These time budget alterations were accompanied by marked changes in theorganization of behavior at the levels of both bout and stateproperties. The circadian variation in AS probability and the underlyingpatterns of ASs and ISs were strikingly different in the WT and OB mice.Although their AS probabilities were similar during a large portion ofthe light cycle (LC), OB mice spent much less time in the AS during theDC than the WT mice. This difference in AS probability was particularlymarked at the beginning and end of the DC. Circadian variation in ASprobability and duration was thus substantially diminished in the OBmice, raising the possibility that their entrainment to the environmentwas impaired. However, OB mice exhibited marked circadian variation inIS duration, suggesting that their capacity to entrain to theirenvironment remained intact but that circadian variation of particularbehaviors was altered.

Consistent with this possibility, OB mice exhibited a crepuscularpattern of food and water intake, with peaks in intake at DC onset andoffset that were similar to those of WT mice. In contrast, circadianvariation in the magnitude of locomotion and “other” behaviors wasstrikingly diminished in OB mice. We found this to result fromphenotypic abnormalities at two levels of behavioral organization: 1)patterns of ASs and 2) within-AS bout rates. With regard to AS patterns,comparison clustering revealed OB mice to have lost the long durationASs normally seen at DC onset and offset. These reductions in ASdurations likely result from selective decreases in locomotion and“other” behaviors at these times. Additionally, OB mice did not exhibitthe marked increases in within-AS bout rates of locomotion and “other”behaviors observed during the DC in WT mice. This suggests that atime-of-day-dependent signal that increases locomotion and “other”behaviors may be reduced in OB mice, or that a competing processinhibits locomotion and “other” behavior without decreasing overall foodand water intake.

Simply examining OB food and water intake at the level of circadianvariation as revealed by changes in amount consumed during two hoursbins indicated that the OB pattern of intake was relatively preserved.In contrast, examination of bout and state properties revealed that themanner in which this pattern of circadian variation was achieved wasmarkedly perturbed. OB mice exhibited consistently larger feeding (OB222 mg WT 38 mg) and drinking (OB 114 mg WT 61 mg) bouts than WT mice.However, the increased bout sizes were accompanied by decreased boutrates resulting in similar intake and crepuscular patterns in both WTand OB mice. For both feeding and drinking, the decreases in intake boutrates resulted primarily from changes in state organization (decreasedAS onset rates accompanied by prolonged IS durations). These dramaticchanges in the regulation of ingestion would not have been revealedwithout the ability to characterize the organization of behavior at thelevels of bout and state properties.

These behavioral alterations likely result from the absence of leptin inOB mice acting as a signal to increase energy intake and decrease energyexpenditure (Friedman and Halaas, 1998). Behaviorally this can bemanifested by increased energy intake and decreased physical activity.We observed that at 3 months of age, OB mice exhibit a small increase infood intake (108% of WT) but a dramatic decrease in movement (17% ofWT). This is consistent with prior work demonstrating that the relativehyperphagia in the OB mice declines with age while the decreases inactivity persist (Joosten and van der Kroon, 1974; Mayer, 1953). It thusappears that decreased movement in the OB mice represents a majorbehavioral alteration contributing to conservation of energy.Accordingly, decreases in movement were apparent at multiple levels ofbehavioral organization. OB mice increased time spent in the IS,decreased time spent in bouts of locomotion, and decreased theirintensity of locomotion.

Signals favoring energy conservation could potentially account for thealtered feeding and drinking patterns observed in the OB mice. Suchsignals, combined with the increased body mass conferred by obesity,could increase the perceived costs of food acquisition in these animals.It is therefore notable that animals respond to experimentalmanipulations that increase the cost of food acquisition (eg, increasedlever presses, exposure to cold) by reducing the number and increasingthe size of feeding clusters (Collier et al., 1972; Johnson and Cabanac,1982; Morato et al., 1995; Petersen and McCarthy, 1981). Thus, patternsof food intake occurring under increased costs bear a marked resemblanceto those of OB mice.

The quantitative description of behavioral patterns in OB mice enablesthe generation of additional testable hypotheses regarding the impact ofleptin on the wide array of processes that shape behavior. For example,animals can conserve energy by eliminating activities, such asreproduction, that are not immediately essential to survival (Ahima etal., 1996). Because male OB mice have very low levels of testosteroneand are infertile (Caprio et al., 2001; Swerdloff et al., 1976),impairments of androgen signaling may play a role in the behavioralpatterns observed in these mice. Consistent with this, gonadectomyincreases feeding bout size and decreases locomotion in male mice andrats (Chai et al., 1999; Perrigo and Bronson, 1985; Petersen, 1978; Royand Wade, 1975). Altogether, it is clear that detailed examination ofhome cage behavior in OB mice reveals alterations of multiple behaviorsat distinct organizational levels. This facilitates the generation oftestable hypotheses regarding the contributions of multipleneuroregulatory systems to these changes.

In contrast with OB mice, the alterations in the organization ofbehavior in 2C mice largely reflected changes in the circadian variationof state patterns (FIGS. 36A-36D & 37A-37D). The 2C mice exhibited adecrease in IS duration throughout the day and a marked increase in theAS onset rate during the LC. Interestingly, the increased food intake inthe 2C mice was restricted to the LC when the AS onset rate and ASprobability were increased. During this time neither feeding AS boutrate nor bout size were increased, thus the increased AS probabilityappears to play an important role in the increased food intake exhibitedby the 2C mice. In fact, comparison clustering revealed that in the 6hours preceding DC onset, the 2C mice exhibited an increase in shortduration ASs (1-5 minutes) that frequently contained feeding boutswithout drinking bouts. The increased LC AS onset rate in the 2C micethus corresponds with an increase in ASs with a high behavioral priorityfor feeding.

While state pattern changes in the 2C mice were most notable, changes inwithin-AS properties were also observed. For example, the 2C mice hadshorter feeding bout durations than the WT mice but they also exhibitedcompensatory increases in bout intensity, resulting in bout sizessimilar to those of WT mice. A similar compensation was observed withregard to locomotion. The 2C mice exhibited decreased locomotion boutdurations but increased bout intensity resulting in similar bout sizes.These findings, as well as the preservation of crepuscular intakepatterns in OB mice reveal that alterations at one level of organizationmay frequently be compensated by changes in another level oforganization to preserve various aspects of the behavioral pattern suchas bout size or circadian intake pattern.

An additional alteration in within-AS properties of 2C mice was alsoobserved. During the DC, 2C mice increased AS bout rates for locomotionand “other” behavior more than WT mice but exhibited a trend towarddecreased AS feeding bout rates. This difference likely accounts for theobservation that the increased LC AS probability in 2C mice wasaccompanied by increased feeding and locomotion but the increased DC ASprobability was only accompanied by increased locomotion. Interestingly,similar circadian influences on feeding and locomotion are seen withadministration of orexin, a neuropeptide produced by neurons of thelateral hypothalamus (LH). During the LC, orexin treatment increasesboth feeding and movement but during the DC orexin only increasesmovement (Espana et al., 2002). The similarity in the circadiandependence of orexin effects and the expression of 5HT2CRs in the LH,raise the possibility that hyperactivity of orexin signaling neurons maycontribute to the 2C phenotype. Other examples of selective LChyperphagia include the increased feeding resulting from VMH lesions(Choi and Dallman, 1999; Choi et al., 1998) and from loss of histamineH1 receptor function (Masaki et al., 2004) suggesting VMH and histaminesystem function as other possible mechanisms that might contribute tothe LC hyperphagia of 2C mice

Alteration in dopamine system function may also play a role in some ofthe phenotypic alterations of 2C mice observed in this study.Previously, 2C mice have been demonstrated to exhibit increasedresponses to novelty and cocaine accompanied by alterations in dopaminesystem function with elevated dopamine levels (Rocha et al., 2002). Theincreased home cage movement observed in the 2C mice may thus resultfrom dopamine system hyperactivity. This may also contribute to theirincreased food intake as hyperdopaminerigic mutant mice (dopaminetransporter knock down) exhibit increased food intake (Pecina et al.,2003). Interestingly, withdrawal from chronic cocaine treatment in ratsresults in a persistent selective LC increase in food intake (Giorgettiand Zhdanova, 2000). Thus, alterations in the functioning of thedopamine system could also contribute to the selective LC increase infood intake in the 2C mice.

Finally, the increased time spent by 2C mice in locomotion and “other”behaviors is intriguing to consider in light of the phenomenon known as“non-exercise activity thermogenesis” (NEAT) (Levine et al., 1999). Inhumans, NEAT refers to all physical activity except purposeful exercise,and includes routine daily activities, such as sitting, standing,walking and fidgeting. Overfeeding was found to increase NEAT, and theextent to which this occurs is highly correlated with weight gain(Levine et al., 1999). Accordingly, obese individuals display reducedNEAT, (increased time sitting and diminished time standing andambulating) even after weight loss (Levine et al., 2005; Ravussin,2005). It has thus been proposed that NEAT levels are innatelydetermined and subject to biological regulation (Levine, 2007). In thiscontext, it is intriguing that time spent engaged in both locomotor andnonlocomotor physical activity is elevated in 3 month old 2C mice. Atthis age, body weights and adiposity levels of 2C mice are normal,despite chronic elevations of food intake (Nonogaki et al., 1998). It istherefore possible that elevations of NEAT enable these animals tomaintain normal body weights. Since both orexinergic and dopaminergicpathways have been implicated in NEAT regulation (Teske et al., 2007),perturbed serotonergic influences on these pathways may contribute toNEAT elevation in 2C mice.

The invention claimed is:
 1. An automated method of analyzing animalbehavioral data collected using a measurement system, said behavioraldata comprising spatial and temporal information regarding positions ofan animal subject in a defined measurement area during a temporalwindow, said method comprising: identifying transitions between activestates and inactive states of the animal subject using the spatial andtemporal information, wherein the entire duration of the temporal windowis divided into the active states and the inactive states, an activestate being a state in which there is a higher probability of aplurality of different types of behaviors indicating activity than in aninactive state, and the inactive state being a state during which theprobability of being in one or more characteristic locations is higherthan a probability during the active state.
 2. The automated method ofclaim 1 further comprising characterizing activity within identifiedactive states as different types of behaviors.
 3. The automated methodof claim 1, wherein inactive states of the animal subject are classifiedduring a time window from animal behavioral data collected over ameasurement period using a measurement system, the animal behavioraldata comprising event information regarding spatial positions of theanimal subject in a measurement area, and wherein the identifyingtransitions between active states and inactive states of the animalsubject comprises: receiving event information regarding the spatialpositions of the animal subject during the time window; analyzing saidevent information to determine information regarding durations of thespatial positions in the time window; determining the longest durationposition (LDP) in the time window; determining information about thespatial distance of every pause location from the longest durationposition (LDP); and using the distance and duration information toclassify inactive states of the animal subject in the time window. 4.The method of claim 3, wherein the animal behavioral data furthercomprises device event information regarding behavior of the animalsubject at or with one or more types of devices at known locations inthe measurement area, the method further comprising identifying anydevice events that occur during the classified inactive states.
 5. Themethod of claim 4, further comprising optimizing the classification ofthe inactive states based on an inactive state error rate.
 6. The methodof claim 5 wherein the inactive state error rate is indicated by theoccurrence of the device events during the identified inactive states.7. The method of claim 4, further comprising reclassifying inactivestates having a device event as active states.
 8. The method of claim 7,further comprising calculating an active state error rate.
 9. The methodof claim 8, wherein calculating an active state error rate comprisescomparing the total area traversed by the animal subject during eachactive state to areas traversed by the animal subject during inactivestates.
 10. The method of claim 3, further comprising identifyinginactive states of the animal subject for multiple time windows in themeasurement period and selecting a time window.
 11. The method of claim3, further comprising: identifying inactive states of the animal subjectfor multiple movement thresholds, wherein a movement threshold definesthe minimum distance between two consecutive positions that must occurfor a change in position location to be considered a movement indetermining the duration of positions; and selecting a movementthreshold.
 12. The automated method of claim 1, further comprisinganalyzing a set of animal subject behavioral data collected over ameasurement period using a measurement system by: receiving positiontracking information for the animal subject in a defined area during themeasurement period and information about temporal patterns of one ormore behaviors during the measurement period; and using the positiontracking information and the temporal information to identify bouts ofthe one or more behaviors.
 13. The automated method of claim 12 furthercomprising receiving device event information regarding behavior of theanimal subject at or with one or more devices at known locations in thedefined measurement area, wherein each device is associated with aparticular behavior.
 14. The method of claim 3, wherein using thedistance and duration information to classify inactive states of theanimal subject in the time window comprises: determining an inactiveposition duration threshold based on information comprising the spatialdistance of every pause location from the LDP and the durations of thespatial positions; using the inactive position duration threshold toclassify inactive states of the animal subject in the time window; andidentifying transitions between active and inactive states.
 15. Themethod of claim 14, wherein using the inactive position durationthreshold to classify inactive states comprises classifying inactivestates as consecutive positions with durations greater than the inactiveposition duration threshold.
 16. The method of claim 12, wherein thebouts of the one or more behaviors comprise bouts of feeding, drinking,locomotor movements, non-locomotor movements, resting, or sleeping. 17.The method of claim 12, further comprising comparing temporal patternsof the active and inactive states across different animals.
 18. Themethod of claim 12, further comprising comparing temporal patterns ofthe bouts of the one or more behaviors across different animals.
 19. Themethod of claim 12, further comprising dissociating temporal patterns ofthe active and inactive states from temporal patterns of the bouts ofthe one or more behaviors.
 20. The method of claim 1, wherein the activestates being states in which there is an increased probability oflocomotion and additional behaviors indicating physical activity and theinactive states being states characterized by increased likelihood ofsleep and rest.
 21. The method of claim 1, wherein the animal subject isclassified as in an inactive state when it stays within a defined areafor a duration longer than an inactive state threshold, and classifiedas being in an active state when it stays within a defined area for aduration shorter than the inactive state threshold.
 22. The method ofclaim 21, wherein the defined area is determined by a movementthreshold.
 23. A method of analyzing animal behavioral data collectedusing a measurement system, said behavioral data comprising spatial andtemporal information regarding spatial positions of an animal subject ina defined measurement area, said method comprising: using the spatialinformation to identify transitions between active and inactive states,which comprises determining an inactive position duration threshold withreference to a longest duration position (LDP), wherein the LDP is thelocation of the longest duration between animal subject movements duringa time period.
 24. The method of claim 23 wherein using the spatialinformation to identify transitions between active and inactive statesfurther comprises determining the relative distances of other positionsof the animal subject during the time period from the longest durationposition (LDP).
 25. The method of claim 23 further comprisingcharacterizing activity within identified active states as differenttypes of behaviors.
 26. The method of claim 25 wherein characterizingactivity within identified active states comprises receiving positiontracking information for the animal subject in a defined area during ameasurement period and information about temporal patterns of one ormore behaviors during the measurement period; and using the positiontracking information and the temporal information to identify bouts ofthe one or more behaviors.
 27. The method of claim 23, wherein saidbehavioral data comprise spatial positions and durations of the spatialpositions of the animal subject during the time period, said methodfurther comprising: determining information about the spatial distancesof the spatial positions from the longest duration position (LDP);determining an inactive position duration threshold based on informationcomprising the spatial distances of the spatial positions from the LDPand the durations of the spatial positions; and classifying inactivestates as consecutive positions with durations greater than the inactiveposition duration threshold.